https://pycon.de/archive/2016/talks/building-and-launching-a-saas-product-with-python-in-2-weeks-a-shopify-app-postm/ 2016-10-28 https://pycon.de/archive/2016/talks/conference-opening/ 2016-10-28 https://pycon.de/archive/2016/talks/connexion-api-first-framework-for-python/ 2016-10-28 https://pycon.de/archive/2016/talks/cynefin-why-do-things-break-and-why-is-it-hard-to-fix-them/ 2016-10-28 https://pycon.de/archive/2016/talks/data-formats-for-data-science/ 2016-10-28 https://pycon.de/archive/2016/talks/debug-like-a-pro-how-to-become-a-better-programmer-through-pdb-driven-developmen/ 2016-10-28 https://pycon.de/archive/2016/talks/deep-modeling-of-dna-sequences-with-python-keras/ 2016-10-28 https://pycon.de/archive/2016/talks/djangoshop-is-back/ 2016-10-28 https://pycon.de/archive/2016/talks/getting-native-with-cython/ 2016-10-28 https://pycon.de/archive/2016/talks/hunting-for-the-best-nosql-database-why-we-love-arangodb/ 2016-10-28 https://pycon.de/archive/2016/talks/infrastructure-as-code-with-aws-cloudformation/ 2016-10-28 https://pycon.de/archive/2016/talks/introduction-to-time-series-analysis-with-pandas/ 2016-10-28 https://pycon.de/archive/2016/talks/lightning-talks/ 2016-10-28 https://pycon.de/archive/2016/talks/lightning-talks-2/ 2016-10-28 https://pycon.de/archive/2016/talks/managing-dependencies-of-python-projects/ 2016-10-28 https://pycon.de/archive/2016/talks/manipulating-and-analysing-multi-dimensional-data-with-pandas/ 2016-10-28 https://pycon.de/archive/2016/talks/mss-software-for-planning-research-aircraft-missions/ 2016-10-28 https://pycon.de/archive/2016/talks/optimize-thyself/ 2016-10-28 https://pycon.de/archive/2016/talks/optimizing-deep-convolutional-neural-networks-for-speed-and-performance/ 2016-10-28 https://pycon.de/archive/2016/talks/plone-and-python-community-a-long-journey/ 2016-10-28 https://pycon.de/archive/2016/talks/powered-by-python-summarizing-hotel-reviews-for-10-000-hotels-and-100-million-tr/ 2016-10-28 https://pycon.de/archive/2016/talks/processing-music-on-the-fly-with-python/ 2016-10-28 https://pycon.de/archive/2016/talks/profiling-the-unprofilable/ 2016-10-28 https://pycon.de/archive/2016/talks/python-at-warp-speed/ 2016-10-28 https://pycon.de/archive/2016/talks/python-for-standalone-applications/ 2016-10-28 https://pycon.de/archive/2016/talks/reliable-and-effective-tools-for-testing-professional-python-code/ 2016-10-28 https://pycon.de/archive/2016/talks/remote-controlling-a-fish-brain-with-python/ 2016-10-28 https://pycon.de/archive/2016/talks/reproducible-science-with-python/ 2016-10-28 https://pycon.de/archive/2016/talks/ssl-all-the-things/ 2016-10-28 https://pycon.de/archive/2016/talks/using-cognitive-services-in-python-projects/ 2016-10-28 https://pycon.de/archive/2016/talks/when-we-were-young-and-in-love/ 2016-10-28 https://pycon.de/archive/2017/talks/an-admin-s-cornucopia-python-is-more-than-just-a-better-bash/ 2017-10-25 https://i.ytimg.com/vi/CQ3qwmld5V8/maxresdefault.jpg An Admin's Cornucopia - Python Is More Than Just A Better Bash Python's versatility is known to admins - in this talk I'd like to show how it fits for many small and big challenges I meet regularly: from tiny scripts to large systems. Also, I'll show how using the languages' advanced and/or newer features makes scripts more compact and robust. https://www.youtube.com/watch?v=CQ3qwmld5V8 https://www.youtube-nocookie.com/embed/CQ3qwmld5V8 yes https://pycon.de/archive/2017/talks/an-introduction-to-pymc3/ 2017-10-25 https://i.ytimg.com/vi/FKhivuCLIT0/maxresdefault.jpg An introduction to PyMC3 PyMC3 allows you to build statistical models for a wide range of datasets, use those models to estimate underlying parameters, and compute the uncertainty about those parameters. In this talk I will try to give a gentle introduction to PyMC3, and help avoid common pitfalls for new users. https://www.youtube.com/watch?v=FKhivuCLIT0 https://www.youtube-nocookie.com/embed/FKhivuCLIT0 yes https://pycon.de/archive/2017/talks/and-now-to-something-else-real-time-data-processing-billiger-de/ 2017-10-25 https://i.ytimg.com/vi/en7XcpYxLU4/maxresdefault.jpg And now to something ELSE: Real Time Data Processing @ billiger.de billiger.de is one of the largest price comparison websites in Germany. In this talk, we want to share how we built the scalable, event-driven processing system which renders the products for our website using Python, Elasticsearch and redis. https://www.youtube.com/watch?v=en7XcpYxLU4 https://www.youtube-nocookie.com/embed/en7XcpYxLU4 yes https://pycon.de/archive/2017/talks/automated-testing-with-400tb-memory/ 2017-10-25 https://i.ytimg.com/vi/Lro5wC_HxhE/maxresdefault.jpg Automated testing with 400TB memory SAP operates a dedicated test infrastructure with more than 400TB main memory for its in-memory database SAP HANA. All custom implementations like improved scheduling, caching of artifacts and monitoring were implemented in our favorite programming language Python. https://www.youtube.com/watch?v=Lro5wC_HxhE https://www.youtube-nocookie.com/embed/Lro5wC_HxhE yes https://pycon.de/archive/2017/talks/building-your-own-sdn-with-debian-linux-salt-stack-and-python/ 2017-10-25 https://i.ytimg.com/vi/yH_0hptXL94/maxresdefault.jpg Building your own SDN with Debian Linux, Salt Stack and Python In this talk you will get an overview about some awesome features of comtemporary Linux networking, how to easily integrate them with some cool open source tools, and glueing all this together with Salt Stack and some Python to get your very own SDN controller for a service-provider style network. https://www.youtube.com/watch?v=yH_0hptXL94 https://www.youtube-nocookie.com/embed/yH_0hptXL94 yes https://pycon.de/archive/2017/talks/connecting-pydata-to-other-big-data-landscapes-using-arrow-and-parquet/ 2017-10-25 https://i.ytimg.com/vi/-IvLScEcoO8/maxresdefault.jpg Connecting PyData to other Big Data Landscapes using Arrow and Parquet While Python itself hosts a wide range of machine learning and data tools, other ecosystems like the Hadoop world also provide beneficial tools that can be either connected via Apache Parquet files or in memory using Arrow. This talks shows recent developments that allow interoperation at speed. https://www.youtube.com/watch?v=-IvLScEcoO8 https://www.youtube-nocookie.com/embed/-IvLScEcoO8 yes https://pycon.de/archive/2017/talks/data-plumbing-101-etl-pipelines-for-everyday-projects/ 2017-10-25 https://i.ytimg.com/vi/D7fYa0NrCuE/maxresdefault.jpg Data Plumbing 101 - ETL Pipelines for Everyday Projects There is no data science without ETL! This presentation is about implementing maintainable data integration for your projects. We will have a first look a ‘Ozelot’, a library based on Luigi and SQLAlchemy that helps you get started with building ETL pipelines. https://www.youtube.com/watch?v=D7fYa0NrCuE https://www.youtube-nocookie.com/embed/D7fYa0NrCuE yes https://pycon.de/archive/2017/talks/data-science-best-practices-from-proof-of-concepts-to-production/ 2017-10-25 https://i.ytimg.com/vi/OPw0VrZdLdo/maxresdefault.jpg Data Science Best Practices : From Proof of Concepts to Production This presentation will benefit the audience as it brings forward the practical issues in the industry today as we move towards industrializing data science algorithms. We will discuss the best practices around organization, methodology and tools to integrate a data science project into production. https://www.youtube.com/watch?v=OPw0VrZdLdo https://www.youtube-nocookie.com/embed/OPw0VrZdLdo yes https://pycon.de/archive/2017/talks/data-science-project-for-beginners/ 2017-10-25 https://i.ytimg.com/vi/4BBCqCgVDLI/maxresdefault.jpg Data Science Project for Beginners AI and Machine Learning are taking over the world - but how do you actually start with understanding your data and predicting events? And what kind of "political" trouble could you run into? With examples from real projects, we try to give you a feeling for data science projects. https://www.youtube.com/watch?v=4BBCqCgVDLI https://www.youtube-nocookie.com/embed/4BBCqCgVDLI yes https://pycon.de/archive/2017/talks/deep-learning-for-computer-vision/ 2017-10-25 https://i.ytimg.com/vi/X4Q6C915sUY/maxresdefault.jpg Deep Learning for Computer Vision The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 to just 2.25% in 2017 (human level error is around 5%). In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’. The talk will give an overview of the cutting edge and some of the core mathematical concepts and will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python… https://www.youtube.com/watch?v=X4Q6C915sUY https://www.youtube-nocookie.com/embed/X4Q6C915sUY yes https://pycon.de/archive/2017/talks/effective-data-analysis-with-pandas-indexes/ 2017-10-25 https://i.ytimg.com/vi/-E2VTtdwT9U/maxresdefault.jpg Effective Data Analysis with Pandas Indexes Pandas is the Swiss-Multipurpose Knife for Data Analysis in Python. In this talk we will look deeper into how to gain productivity utilizing Pandas powerful indexing and make advanced analytics a piece of cake. Pandas features multiple index types. This talk will give you a deep insight into the Pandas indexes and showcase the handiness of special Indexes as the TimeSeriesIndex. https://www.youtube.com/watch?v=-E2VTtdwT9U https://www.youtube-nocookie.com/embed/-E2VTtdwT9U yes https://pycon.de/archive/2017/talks/empowered-by-python-a-success-story/ 2017-10-25 https://i.ytimg.com/vi/2Ku3tV3QQ3M/maxresdefault.jpg Empowered by Python - A success story Introducing a new programming language in a company is always a daring task, usually involving a lot of effort and the will for change. We'd like to take you on a journey reflecting eight years of challenges, solutions and success ending in a best practice guide helping you to achieve the same. https://www.youtube.com/watch?v=2Ku3tV3QQ3M https://www.youtube-nocookie.com/embed/2Ku3tV3QQ3M yes https://pycon.de/archive/2017/talks/flow-is-in-the-air-best-practices-of-building-analytical-data-pipelines-with-apa/ 2017-10-25 https://i.ytimg.com/vi/Ea3smcbnGxE/maxresdefault.jpg Flow is in the Air: Best Practices of Building Analytical Data Pipelines with Apache Airflow Apache Airflow is an Open-Source python project which facilitates an intuitive programmatic definition of analytical data pipelines. Based on 2+ years of productive experience, we summarize its core concepts, detail on lessons learned and set it in context with the Big Data Analytics Ecosystem. https://www.youtube.com/watch?v=Ea3smcbnGxE https://www.youtube-nocookie.com/embed/Ea3smcbnGxE yes https://pycon.de/archive/2017/talks/from-0-to-continuous-delivery-in-30-minutes/ 2017-10-25 https://i.ytimg.com/vi/yqpOrB0JDto/maxresdefault.jpg From 0 to Continuous Delivery in 30 minutes. An introduction and hands on example how to start Continuous Delivery for python (or whatever) projects with conda and gitlab, which are open source, free to use, and if you wish even available as a cloud service. https://www.youtube.com/watch?v=yqpOrB0JDto https://www.youtube-nocookie.com/embed/yqpOrB0JDto yes https://pycon.de/archive/2017/talks/from-java-to-python-migrating-search-functionality-at-billiger-de/ 2017-10-25 https://i.ytimg.com/vi/2fuW9ITUXrU/maxresdefault.jpg From Java to Python: Migrating Search Functionality at billiger.de billiger.de is a German price comparison site. Search is handled by a heavily customized Solr setup. When switching to SolrCloud earlier this year, instead of porting our custom SolrComponents to SolrCloud, we ended up re-implementing them in a Python service layer. Here we show how, and why. https://www.youtube.com/watch?v=2fuW9ITUXrU https://www.youtube-nocookie.com/embed/2fuW9ITUXrU yes https://pycon.de/archive/2017/talks/getting-scikit-learn-to-run-on-top-of-pandas/ 2017-10-25 https://i.ytimg.com/vi/boXOVvu43ZI/maxresdefault.jpg Getting Scikit-Learn To Run On Top Of Pandas Scikit-Learn is built directly over numpy, Python's numerical array library. Pandas adds to numpy metadata and higher-level munging capabilities. This talk describes how to intelligently auto-wrap Scikit-Learn for creating a version that can leverage pandas's added features. https://www.youtube.com/watch?v=boXOVvu43ZI https://www.youtube-nocookie.com/embed/boXOVvu43ZI yes https://pycon.de/archive/2017/talks/graphql-in-the-python-world/ 2017-10-25 https://i.ytimg.com/vi/FpQpF0BTrJU/maxresdefault.jpg Graphql in the Python World So you've heard about this new thing called Graphql. What is it all about? What problems does it solve, and most importantly, how can you leverage it the python ecosystem? This talk is a tell all on what what Graphql is and how you can start using it with Python. https://www.youtube.com/watch?v=FpQpF0BTrJU https://www.youtube-nocookie.com/embed/FpQpF0BTrJU yes https://pycon.de/archive/2017/talks/hacking-the-python-ast/ 2017-10-25 https://i.ytimg.com/vi/kaxAF542Cic/maxresdefault.jpg Hacking the Python AST Computer languages are a remarkable feat of human scientific engineering. In this talk, we'll look at the innards of CPython, and specifically learn how to modify and hack Abstract Syntax Trees (for world peace, of course). https://www.youtube.com/watch?v=kaxAF542Cic https://www.youtube-nocookie.com/embed/kaxAF542Cic yes https://pycon.de/archive/2017/talks/high-performance-ingestion-with-python-and-swarm64db/ 2017-10-25 https://i.ytimg.com/vi/L4EdHKLB_08/maxresdefault.jpg High-Performance Ingestion with Python and Swarm64DB Swarm64DB is a hardware-accelerated plugin for PostgreSQL and other RDBMS. By using Swarm64DB in combination with PostgreSQL, Python and the right scaling mechanism, we are able to push the ingestion throughput into areas where Python can easily compete with compiled languages. The talk highlights the architecture of our solution and showcases a real world use-case.. https://www.youtube.com/watch?v=L4EdHKLB_08 https://www.youtube-nocookie.com/embed/L4EdHKLB_08 yes https://pycon.de/archive/2017/talks/how-efficient-is-your-public-transport-network-a-data-driven-approach-using-geop/ 2017-10-25 https://pycon.de/archive/2017/talks/how-to-fund-your-company/ 2017-10-25 https://pycon.de/archive/2017/talks/integrating-jupyter-notebooks-into-your-infrastructure/ 2017-10-25 https://i.ytimg.com/vi/xplmuHEFqCg/maxresdefault.jpg Integrating Jupyter Notebooks into your Infrastructure Jupyter Notebooks combine executable code and rich text elements in a web application. In this talk you will learn how a custom JupyterHub installation can be used to integrate Jupyter Notebooks into your infrastructure, including existing authentication methods and custom software distributions. https://www.youtube.com/watch?v=xplmuHEFqCg https://www.youtube-nocookie.com/embed/xplmuHEFqCg yes https://pycon.de/archive/2017/talks/keeping-the-grip-on-decoupled-code-using-clis/ 2017-10-25 https://i.ytimg.com/vi/F20vrgQCFMs/maxresdefault.jpg Keeping the grip on decoupled code using CLIs So you’ve decoupled your code monolith into all those micro chunks. When someone asks „How can I…“ you want to answer: „That’s easy! We’ve built that.“ Actually, you’ve built all parts needed for that. Who plugs them together? And how? https://www.youtube.com/watch?v=F20vrgQCFMs https://www.youtube-nocookie.com/embed/F20vrgQCFMs yes https://pycon.de/archive/2017/talks/large-scale-machine-learning-pipelines-using-luigi-pyspark-and-scikit-learn/ 2017-10-25 https://i.ytimg.com/vi/VFB0rcfFCbg/maxresdefault.jpg Large-scale machine learning pipelines using Luigi, PySpark and scikit-learn For prescriptive analytics applications, data science teams need to design, build and maintain complex machine learning pipelines. In this talk, we demonstrate how such pipelines can be implemented in a robust, scalable and extensible manner using Python, Luigi, PySpark and scikit-learn. https://www.youtube.com/watch?v=VFB0rcfFCbg https://www.youtube-nocookie.com/embed/VFB0rcfFCbg yes https://pycon.de/archive/2017/talks/lift-your-speed-limits-with-cython/ 2017-10-25 https://i.ytimg.com/vi/nTKQkm8U0zE/maxresdefault.jpg Lift your Speed Limits with Cython Think you can benefit from making your Python application run faster? Then come along and learn how to tune your code with Cython. https://www.youtube.com/watch?v=nTKQkm8U0zE https://www.youtube-nocookie.com/embed/nTKQkm8U0zE yes https://pycon.de/archive/2017/talks/machine-learning-as-a-service/ 2017-10-25 https://pycon.de/archive/2017/talks/master-2-5-gb-of-unstructured-specification-documents-with-ease/ 2017-10-25 https://i.ytimg.com/vi/g277gRcG84I/maxresdefault.jpg Master 2.5 GB of unstructured specification documents with ease How Do you kick start a project which is based on 2.5 GB files of unstructured specification documents? To answer this question, we present our lessons learned from developing a Python based knowledge management tool which provides a lightweight and intuitive browser frontend. https://www.youtube.com/watch?v=g277gRcG84I https://www.youtube-nocookie.com/embed/g277gRcG84I yes https://pycon.de/archive/2017/talks/matthew-rocklin/ 2017-10-25 https://i.ytimg.com/vi/rZlshXJydgQ/maxresdefault.jpg Dask: Next Steps in Parallel Python Dask: Next Steps in Parallel Python https://www.youtube.com/watch?v=rZlshXJydgQ https://www.youtube-nocookie.com/embed/rZlshXJydgQ yes https://pycon.de/archive/2017/talks/metaclasses-when-to-use-and-when-not-to-use/ 2017-10-25 https://pycon.de/archive/2017/talks/michael-feindt/ 2017-10-25 https://i.ytimg.com/vi/Ndi4hqSdBi4/maxresdefault.jpg Artificial Intelligence: Differentiating Hype and Real Value Starting from the evolution of biological and artificial life and intelligence examples of already existing superhuman performance of "narrow" AI are presented. The history of artificial neural networks nicely shows how wrong human experts can be. Conditions for and principles of predictive and prescriptive analytics, two important working horses of AI, are presented. It is explained why superhuman performance is especially possible in mass decisions under uncertainty. Their value is proven with examples from largely improved and automated public scientific research as well as decision making in enterprises. A personal view about the role of python and some selected topics that will gain much more attention like causality extraction from historic data and discrimination-free algorithms are given, before concluding on how we should face the challenges but especially the chances of AI to create a better world. https://www.youtube.com/watch?v=Ndi4hqSdBi4 https://www.youtube-nocookie.com/embed/Ndi4hqSdBi4 yes https://pycon.de/archive/2017/talks/migrating-existing-codebases-to-using-type-annotations/ 2017-10-25 https://i.ytimg.com/vi/JKvtrb2GWMY/maxresdefault.jpg Migrating existing codebases to using type annotations You have an existing codebase of tens or hundreds of thousands of lines of Python code? Learn how to get started with type annotations! Get your teammates (and yourself!) to always annotate your code. Find out what unexpected issues you might run into and how to solve them, all with this talk. https://www.youtube.com/watch?v=JKvtrb2GWMY https://www.youtube-nocookie.com/embed/JKvtrb2GWMY yes https://pycon.de/archive/2017/talks/modern-etl-ing-with-python-and-airflow-and-spark/ 2017-10-25 https://i.ytimg.com/vi/tcJhSaowzUI/maxresdefault.jpg Modern ETL-ing with Python and Airflow (and Spark) The challenge of data integration is real. The sheer amount of tools that exist to address this problem is proof that organizations struggle with it. This talk will discuss the inherent challenges of data integration, and show how it can be tackled using Python and Apache Airflow and Apache Spark. https://www.youtube.com/watch?v=tcJhSaowzUI https://www.youtube-nocookie.com/embed/tcJhSaowzUI yes https://pycon.de/archive/2017/talks/network-analysis-using-python/ 2017-10-25 https://pycon.de/archive/2017/talks/no-compromise-use-ansible-properly-or-stick-to-your-scripts/ 2017-10-25 https://i.ytimg.com/vi/7qipNlReXYg/maxresdefault.jpg No Compromise: Use Ansible properly or stick to your scripts What you do in Ansible should be clean an simple. What we did was not. So I will show what we did wrong but also what we have changed or still have to, to make our life easier again. But I will also show how we progressively utilize Ansible to deploy our Data Science infrastructure. https://www.youtube.com/watch?v=7qipNlReXYg https://www.youtube-nocookie.com/embed/7qipNlReXYg yes https://pycon.de/archive/2017/talks/observing-your-applications-with-sentry-and-prometheus/ 2017-10-25 https://i.ytimg.com/vi/f3WO4bpLySs/maxresdefault.jpg Observing your applications with Sentry and Prometheus If you have services running in production, something will fail sooner or later. We cannot avoid this completely, but we can prepare for it. In this talk we will have a look at how Sentry and Prometheus can help to get better insight into our systems to quickly track down the cause of failure. https://www.youtube.com/watch?v=f3WO4bpLySs https://www.youtube-nocookie.com/embed/f3WO4bpLySs yes https://pycon.de/archive/2017/talks/platform-intrusion-detection-with-deep-learning/ 2017-10-25 https://i.ytimg.com/vi/dXqBuZi7JOE/maxresdefault.jpg Platform intrusion detection with deep learning shop.rewe.de is not only visited by human customers, but also by machines. We have built a deep learning platform using python with Keras, Tensorflow, on the Google infrastructure. In this talk we would like to show you how python is used in practice, supporting 2,5 million visitors each day. https://www.youtube.com/watch?v=dXqBuZi7JOE https://www.youtube-nocookie.com/embed/dXqBuZi7JOE yes https://pycon.de/archive/2017/talks/playing-with-google-ml-apis-and-websockets/ 2017-10-25 https://pycon.de/archive/2017/talks/plugin-ecosystems-for-python-web-applications/ 2017-10-25 https://i.ytimg.com/vi/5NxRdzLTFik/maxresdefault.jpg Plugin ecosystems for Python web-applications The power of some popular web applications like WordPress comes from a flexible plugin system. This talk will show how to implement such plugin architectures for Python web applications including real-world examples. I'll give examples with Django, but the important bits aren't Django-specific. https://www.youtube.com/watch?v=5NxRdzLTFik https://www.youtube-nocookie.com/embed/5NxRdzLTFik yes https://pycon.de/archive/2017/talks/practical-data-cleaning-101/ 2017-10-25 https://pycon.de/archive/2017/talks/programming-the-web-of-things-with-python-and-micropython/ 2017-10-25 https://i.ytimg.com/vi/JtsLlYvcRJI/maxresdefault.jpg Programming the Web of Things with Python and MicroPython In this session you will get a gentle introduction to the ever-expanding world of small programmable devices: learn to use single board computers and microcontrollers to connect to sensors and talk to APIs - all using Python or MicroPython, a subset of Python 3 for use in constrained environments. https://www.youtube.com/watch?v=JtsLlYvcRJI https://www.youtube-nocookie.com/embed/JtsLlYvcRJI yes https://pycon.de/archive/2017/talks/project-avatar-telepresence-robotics-with-nao-and-kinect/ 2017-10-25 https://i.ytimg.com/vi/SGpdc-9QAWE/maxresdefault.jpg Project Avatar - Telepresence robotics with Nao and Kinect Using humanoid robots, VR glasses and 3D cameras you can experience the world through the eyes of a robot and control it via gestures. We built a telepresence robotics system based on a Nao robot, an Oculus Rift and a Kinect One to realize an immersive "out-of-body experience" as in "Avatar". https://www.youtube.com/watch?v=SGpdc-9QAWE https://www.youtube-nocookie.com/embed/SGpdc-9QAWE yes https://pycon.de/archive/2017/talks/python-in-space-the-n-body-problem/ 2017-10-25 https://i.ytimg.com/vi/E1nx4ReTgps/maxresdefault.jpg Python in Space - The N Body Problem The N Body Problem is a computationally complex problem that we use to predict how planets and galaxies – and everything in between – move through space. I'll show you some interesting ways to calculate it, and we'll have a look at what to do, should you find yourself in a space ship's pilot seat. https://www.youtube.com/watch?v=E1nx4ReTgps https://www.youtube-nocookie.com/embed/E1nx4ReTgps yes https://pycon.de/archive/2017/talks/python-is-weird/ 2017-10-25 https://i.ytimg.com/vi/zz9yLOXo2Qk/maxresdefault.jpg Python is Weird A lot of people think that Python is a really simple and straightforward language. Python hides a lot of peculiarities very well, but for the sake of this talk we will try to uncover them. Is ++4; valid Python? And what does it do? Let me give you an introduction into tokenizers/parsers. https://www.youtube.com/watch?v=zz9yLOXo2Qk https://www.youtube-nocookie.com/embed/zz9yLOXo2Qk yes https://pycon.de/archive/2017/talks/python-on-bare-metal-beginners-tutorial-with-micropython-on-the-pyboard/ 2017-10-25 https://i.ytimg.com/vi/MpRXnyFeEwg/maxresdefault.jpg Python on bare metal – Beginners tutorial with MicroPython on the pyboard MicroPython is a complete reimplementation of Python that runs on small devices like microcontrollers. In this hands-on workshop I'll show how easy it is to use MicroPython on a pyboard. https://www.youtube.com/watch?v=MpRXnyFeEwg https://www.youtube-nocookie.com/embed/MpRXnyFeEwg yes https://pycon.de/archive/2017/talks/python-with-apache-openwhisk/ 2017-10-25 https://i.ytimg.com/vi/E9Yj4g9LuJc/maxresdefault.jpg Python with Apache OpenWhisk OpenWhisk is an opensource implementation of a so called serverless computing platform. At a live presentation I will show how to write an serverless application and how to deal with libraries and events. OpenWhisk is an open source alternative to AWS lambda or MS functions. https://www.youtube.com/watch?v=E9Yj4g9LuJc https://www.youtube-nocookie.com/embed/E9Yj4g9LuJc yes https://pycon.de/archive/2017/talks/rasa-open-source-conversational-ai-to-build-next-generation-chatbots/ 2017-10-25 https://i.ytimg.com/vi/LEFF7-_uh3M/maxresdefault.jpg Rasa: open source conversational AI to build next generation chatbots Soon you will primarily communicate with your computer through conversation. At Rasa, we believe that this revolution in user experience should be available to everyone. In this spirit we have developed open source tools that use machine learning to make chatbots in a developer-friendly interface. https://www.youtube.com/watch?v=LEFF7-_uh3M https://www.youtube-nocookie.com/embed/LEFF7-_uh3M yes https://pycon.de/archive/2017/talks/really-deep-neural-networks-with-pytorch/ 2017-10-25 https://i.ytimg.com/vi/ZUHhNuw9Tlc/maxresdefault.jpg Really Deep Neural Networks with PyTorch Modern neural networks have hundreds of layers! How can we train such deep networks? Simply stacking layers on top doesn't work! This talk introduces the deep learning library PyTorch by explaining the exciting math, cool ideas and simple code behind what makes really deep neural networks work. https://www.youtube.com/watch?v=ZUHhNuw9Tlc https://www.youtube-nocookie.com/embed/ZUHhNuw9Tlc yes https://pycon.de/archive/2017/talks/simple-data-engineering-in-python-3-5-with-bonobo/ 2017-10-25 https://i.ytimg.com/vi/3agWJTRn2cc/maxresdefault.jpg Simple Data Engineering in python 3.5+ with Bonobo Simple is better than complex, and that's True for data pipelines, too. Bonobo is a python 3.5+ tool used to write and monitor data pipelines. It’s plain, simple, modern, and atomic python. This talk is a practical encounter, from zero to a complete data pipeline. Spoiler : no «big data» here. https://www.youtube.com/watch?v=3agWJTRn2cc https://www.youtube-nocookie.com/embed/3agWJTRn2cc yes https://pycon.de/archive/2017/talks/sport-analysis-with-python/ 2017-10-25 https://i.ytimg.com/vi/Fl6YFdf37IE/maxresdefault.jpg Sport analysis with Python Sport analysis with Python and visualize data with tableau. We have sample data of a team in football match (name of players, positions of players, velocities of players) which are recorded in every 20 millisecond. We use python to analysis and Tableau to visualize the activities of each player https://www.youtube.com/watch?v=Fl6YFdf37IE https://www.youtube-nocookie.com/embed/Fl6YFdf37IE yes https://pycon.de/archive/2017/talks/susanne-mertens/ 2017-10-25 https://i.ytimg.com/vi/3h-6GBBF4Hg/maxresdefault.jpg Neutrinos: who are you and if yes how many? The neutrino is a strange particle: it it extremely light and flies through matter without leaving a trace. Nevertheless --due to its vast abundance-- it plays a key role as cosmological architect in the formation of galaxies in our universe. One of the missing puzzle pieces for the exact understanding of the evolution of the universe is the mass of the neutrino. The discovery of neutrino oscillations, awarded with the Nobel prize in 2015, proofs that neutrinos are not massless, but does not reveal its actual value. The Karlsruhe Tritium Neutrino (KATRIN) experiment aims at directly measuring the neutrino mass by investigating the radioactive decay of tritium with unprecedented precision. The talk will report on the mysterious neutrinos, how KATRIN will track down their mass, and which role computational methods play in the realization of such a large-scale experiment. https://www.youtube.com/watch?v=3h-6GBBF4Hg https://www.youtube-nocookie.com/embed/3h-6GBBF4Hg yes https://pycon.de/archive/2017/talks/synthetic-data-for-machine-learning-applications/ 2017-10-25 https://i.ytimg.com/vi/riT9KTkBj0E/maxresdefault.jpg Synthetic Data for Machine Learning Applications In this talk I will show how we use real and synthetic data to create successful models for risk assessing pipeline anomalies. The main focus is the estimation of the difference in the statistical properties of real and generated data by machine learning methods. https://www.youtube.com/watch?v=riT9KTkBj0E https://www.youtube-nocookie.com/embed/riT9KTkBj0E yes https://pycon.de/archive/2017/talks/technical-lessons-learned-from-pythonic-refactoring/ 2017-10-25 https://i.ytimg.com/vi/Yq9-b2JKUyU/maxresdefault.jpg Technical Lessons Learned from Pythonic Refactoring Ever stumbled upon poorly-maintained codebases that suck away your productivity? Fear no more! This talk addresses how to identify code smell (from Brie to Bleu cheese) and go through examples to refactor code and APIs. You will learn the art of writing clean, maintainable and idiomatic Python code. https://www.youtube.com/watch?v=Yq9-b2JKUyU https://www.youtube-nocookie.com/embed/Yq9-b2JKUyU yes https://pycon.de/archive/2017/talks/the-borgbackup-project/ 2017-10-25 https://i.ytimg.com/vi/oLFMsP1GMa0/maxresdefault.jpg The BorgBackup Project BorgBackup is a modern, deduplicating backup software written in Python 3.4+, Cython and C. The talk will start with a quick presentation about the software and why you may want to use it for your backups. Then, I will show how we run the software project: Tools, Services, Best Practices. https://www.youtube.com/watch?v=oLFMsP1GMa0 https://www.youtube-nocookie.com/embed/oLFMsP1GMa0 yes https://pycon.de/archive/2017/talks/the-eye-of-the-python-an-eye-tracking-system-from-zero-to-what-eye-learned/ 2017-10-25 https://i.ytimg.com/vi/JckAPb-HpME/maxresdefault.jpg The eye of the Python, an eye tracking system. From zero to... what eye learned. Is it possible to predict the point in the screen where a person is looking at? Easy to say but hard to do. An eye tracking system is the perfect project to learn the difficulties of applied machine learning. From gathering training data to building the final software with an acceptable performance. https://www.youtube.com/watch?v=JckAPb-HpME https://www.youtube-nocookie.com/embed/JckAPb-HpME yes https://pycon.de/archive/2017/talks/the-mustache-movement/ 2017-10-25 https://i.ytimg.com/vi/9lVbpzd1hWk/maxresdefault.jpg The Mustache Movement Generative Adversarial Networks (GANs) are a class of neural networks which are powerful and flexible tools. A common application is image generation. I would like to give a simple introduction to GANs using existing python modules and an example of how "mustache-ness" can be learned and applied. https://www.youtube.com/watch?v=9lVbpzd1hWk https://www.youtube-nocookie.com/embed/9lVbpzd1hWk yes https://pycon.de/archive/2017/talks/the-python-ecosystem-for-data-science-a-guided-tour/ 2017-10-25 https://i.ytimg.com/vi/BIWcciNeMm0/maxresdefault.jpg The Python Ecosystem for Data Science: A Guided Tour Pythonistas have access to an extensive collection of tools for data analysis. The space of tools is best understood as an ecosystem: Libraries build upon each other, and a good library fills an ecological niche by doing certain jobs well. This is a guided tour of the Python data science ecosystem. https://www.youtube.com/watch?v=BIWcciNeMm0 https://www.youtube-nocookie.com/embed/BIWcciNeMm0 yes https://pycon.de/archive/2017/talks/the-snake-in-the-tar-pit-complex-systems-with-python/ 2017-10-25 https://i.ytimg.com/vi/pee-e01DiyI/maxresdefault.jpg The Snake in the Tar Pit: Complex Systems with Python The Zen of Python motivates us to build software that is easy to maintain and extend. In reality however, we often end up with systems that are quite the opposite: complex and hard to change. In this talk, we will have a look at why this happens and how we can try to prevent it. https://www.youtube.com/watch?v=pee-e01DiyI https://www.youtube-nocookie.com/embed/pee-e01DiyI yes https://pycon.de/archive/2017/talks/theoretical-physics-with-sympy/ 2017-10-25 https://i.ytimg.com/vi/Ax0et1ZOOTc/maxresdefault.jpg Theoretical physics with sympy In this talk, I will introduce the basics of sympy. Using a simple model system in magnetism, we'll play around with simplifications, then do a bit of numerical optimization and in the end make psychedelic-looking figures. https://www.youtube.com/watch?v=Ax0et1ZOOTc https://www.youtube-nocookie.com/embed/Ax0et1ZOOTc yes https://pycon.de/archive/2017/talks/time-series-feature-extraction-with-tsfresh-get-rich-or-die-overfitting/ 2017-10-25 https://i.ytimg.com/vi/Fm8zcOMJ-9E/maxresdefault.jpg Time series feature extraction with tsfresh - “get rich or die overfitting” Have you ever thought about developing a time series model to predict stock prices? Or do you consider log time series from the operation of cloud resources as being more compelling? In this case you really should consider using the time series feature extraction package tsfresh for your project. https://www.youtube.com/watch?v=Fm8zcOMJ-9E https://www.youtube-nocookie.com/embed/Fm8zcOMJ-9E yes https://pycon.de/archive/2017/talks/turbodbc-turbocharged-database-access-for-data-scientists/ 2017-10-25 https://i.ytimg.com/vi/B-uj8EDcjLY/maxresdefault.jpg Turbodbc: Turbocharged database access for data scientists Python's database API 2.0 is well suited for transactional database workflows, but not so much for column-heavy data science. This talk explains how the ODBC-based turbodbc database module extends this API with first-class, efficient support for familiar NumPy and Apache Arrow data structures. https://www.youtube.com/watch?v=B-uj8EDcjLY https://www.youtube-nocookie.com/embed/B-uj8EDcjLY yes https://pycon.de/archive/2017/talks/verified-fakes-with-openapi/ 2017-10-25 https://i.ytimg.com/vi/NZqovz37Qcw/maxresdefault.jpg Verified fakes with OpenAPI It can be hard to test code that depends on external services. Often such services are mocked, but with time, it can be challenging to keep these mocks up to date. Verified fakes can solve this problem, and we will see how to set them up using OpenAPI and python. https://www.youtube.com/watch?v=NZqovz37Qcw https://www.youtube-nocookie.com/embed/NZqovz37Qcw yes https://pycon.de/archive/2017/talks/vim-your-python-python-your-vim/ 2017-10-25 https://i.ytimg.com/vi/prnndyNV60w/maxresdefault.jpg Vim your Python, Python your Vim What do you use to write source code, docs, books or e-mails? Single brain, single pair of hands, single keyboard, but a different keyboard layout for each language and a different text editor for each purpose? https://www.youtube.com/watch?v=prnndyNV60w https://www.youtube-nocookie.com/embed/prnndyNV60w yes https://pycon.de/archive/2017/talks/why-python-has-taken-over-finance/ 2017-10-25 https://i.ytimg.com/vi/aXh7K6cFA8g/maxresdefault.jpg Why Python Has Taken Over Finance Not too long ago, the finance field was dominated by compiled languages, such as C or C++, since they were considered to be the right choice for the implementation of computationally demanding algorithms. This talk explains why Python has become No 1 in the field. https://www.youtube.com/watch?v=aXh7K6cFA8g https://www.youtube-nocookie.com/embed/aXh7K6cFA8g yes https://pycon.de/archive/2018/talks/a-day-has-only-241-hours-import-pytz/ 2018-11-05 https://pycon.de/static/media/social/talks/7BJQJB.png A Day Has Only 24±1 Hours: import pytz https://i.ytimg.com/vi/IRGKlwkio0Y/maxresdefault.jpg A Day Has Only 24±1 Hours: import pytz Handle easily all those timezone issues your system knows about. Fear those it doesn't. https://www.youtube.com/watch?v=IRGKlwkio0Y https://www.youtube-nocookie.com/embed/IRGKlwkio0Y 2018-11-05 yes https://pycon.de/archive/2018/talks/about-going-open-source/ 2018-11-05 https://pycon.de/static/media/social/talks/3RMPDC.png About going Open-Source https://i.ytimg.com/vi/A_UtST302Og/maxresdefault.jpg About going Open-Source Why should you work for free? This talk is about one of the best decisions in my life... Starting to get into Open-Source. You will get a gentle introduction on what makes Open-Source so satisfying and the ways you can use to get started with Open-Source. Whether you're already a full grown developer or just starting out, this talk will probably give you some ideas and the motivation to really become passionate. To close this off, you will get a little impression on how important an Open-Source portfolio is to your career and I'll give some neat hints to get your projects to the next level. So join me and start Your journey! https://www.youtube.com/watch?v=A_UtST302Og https://www.youtube-nocookie.com/embed/A_UtST302Og 2018-11-05 yes https://pycon.de/archive/2018/talks/achieving-resilient-code-with-integration-tests/ 2018-11-05 https://pycon.de/static/media/social/talks/V3UZWL.png Achieving Resilient Code with Integration Tests https://i.ytimg.com/vi/qRJ5qhblXV8/maxresdefault.jpg Achieving Resilient Code with Integration Tests Writing good and isolated integration tests can be challenging. We will see in this talk how to reach this goal, using Pytest and Docker Compose. https://www.youtube.com/watch?v=qRJ5qhblXV8 https://www.youtube-nocookie.com/embed/qRJ5qhblXV8 2018-11-05 yes https://pycon.de/archive/2018/talks/active-learning-building-semi-supervised-classifiers-when-labeled-data-is-not-available/ 2018-05-24 https://pycon.de/static/media/social/talks/EQA7TS.png Active Learning - Building Semi-supervised Classifiers when Labeled Data is not Available https://pycon.de/archive/2018/talks/advanced-analytics-today-from-open-source-integration-to-the-operationalization-of-the-analytic-lifecycle/ 2018-09-06 https://pycon.de/static/media/social/talks/KCTMMV.png Advanced Analytics Today: From Open Source Integration to the Operationalization of the Analytic Lifecycle https://pycon.de/archive/2018/talks/beyond-jupyter-notebooks-building-your-own-data-science-platform-with-python-docker/ 2018-07-01 https://pycon.de/static/media/social/talks/C9VEMT.png Beyond Jupyter Notebooks - Building your own Data Science platform with Python & Docker https://pycon.de/archive/2018/talks/big-data-systems-performance-the-little-shop-of-horrors/ 2018-11-05 https://pycon.de/static/media/social/talks/HBNNF8.png Big Data Systems Performance: The Little Shop of Horrors https://i.ytimg.com/vi/FLTC2N3xrug/maxresdefault.jpg Big Data Systems Performance: The Little Shop of Horrors The confusion around terms such as like NoSQL, Big Data, Data Science, SQL, Spark, and Data Lakes often creates more fog than clarity. In my presentation, I will show that often at least three design dimensions are cluttered and confused in discussions when it comes to data management and how understanding these dfimensions helps you making your applications several orders of magnitude faster. https://www.youtube.com/watch?v=FLTC2N3xrug https://www.youtube-nocookie.com/embed/FLTC2N3xrug 2018-11-05 yes https://pycon.de/archive/2018/talks/binder-lowering-the-bar-to-sharing-interactive-software/ 2018-11-05 https://pycon.de/static/media/social/talks/A9HL7W.png Binder - lowering the bar to sharing interactive software https://i.ytimg.com/vi/TQY_fVPEWTw/maxresdefault.jpg Binder - lowering the bar to sharing interactive software In this talk I will introduce the audience to the concepts and ideas behind the Binder project. I will showcase examples from the community to illustrate use-cases and show off the power of Binder using the mybinder.org service. https://www.youtube.com/watch?v=TQY_fVPEWTw https://www.youtube-nocookie.com/embed/TQY_fVPEWTw 2018-11-05 yes https://pycon.de/archive/2018/talks/bonobo-airflow-and-grafana-to-visualize-your-business/ 2018-11-05 https://pycon.de/static/media/social/talks/NJUTQM.png Bonobo, Airflow and Grafana to visualize your business https://i.ytimg.com/vi/UUrk1K45Euw/maxresdefault.jpg Bonobo, Airflow and Grafana to visualize your business Build a simple business intelligence dashboard using python and open-source tools. https://www.youtube.com/watch?v=UUrk1K45Euw https://www.youtube-nocookie.com/embed/UUrk1K45Euw 2018-11-05 yes https://pycon.de/archive/2018/talks/build-a-modern-data-infrastructure/ 2018-05-27 https://pycon.de/static/media/social/talks/8WXEH8.png Build a modern data infrastructure https://pycon.de/archive/2018/talks/build-text-classification-models-cbow-and-skip-gram-with-fasttext-in-python/ 2018-05-27 https://pycon.de/static/media/social/talks/GJ9AWL.png Build text classification models ( CBOW and Skip-gram) with FastText in python https://pycon.de/archive/2018/talks/building-your-own-conversational-ai-with-open-source-tools/ 2018-11-05 https://pycon.de/static/media/social/talks/QU7PLA.png Building your own conversational AI with open source tools https://i.ytimg.com/vi/vVco861PUJo/maxresdefault.jpg Building your own conversational AI with open source tools Point-and-click tools like Dialogflow and LUIS are great for building simple prototypes of conversational agents. But they don’t scale well beyond answering simple questions. In this live-coding talk, you will learn the fundamentals of conversational AI and how to build your own that can handle more complex back-and-forth conversations using open source libraries. https://www.youtube.com/watch?v=vVco861PUJo https://www.youtube-nocookie.com/embed/vVco861PUJo 2018-11-05 yes https://pycon.de/archive/2018/talks/case-study-in-travel-business-understanding-agent-connections-using-networkx/ 2018-05-22 https://pycon.de/static/media/social/talks/F8NLRH.png Case Study in Travel Business - Understanding agent connections using NetworkX https://pycon.de/archive/2018/talks/cloud-chat-bot-for-lazy-people/ 2018-11-05 https://pycon.de/static/media/social/talks/HW3RYU.png Cloud chat bot for lazy people https://i.ytimg.com/vi/oLCy7UrD-gU/maxresdefault.jpg Cloud chat bot for lazy people These days with chat applications everywhere people tend to ask other people about things there although a direct answer would not be far on a website or it would be the first Google search result. It is understandable because mankind is lazy, so I am. Therefore, I want to let bots answer the easy questions for me. In my situation as a DevOps person these questions can be about the health of a service I am responsible for. I will show you how you can run a Python bot with [Azure bot service]( https://azure.microsoft.com/en-us/services/bot-service/), reachable by [Slack]( https://slack.com/) which gives you a service health status based on information from a [Prometheus](https://prometheus.io/) monitoring system. https://www.youtube.com/watch?v=oLCy7UrD-gU https://www.youtube-nocookie.com/embed/oLCy7UrD-gU 2018-11-05 yes https://pycon.de/archive/2018/talks/concurrency-in-python-concepts-frameworks-and-best-practices/ 2018-11-05 https://pycon.de/static/media/social/talks/N8MZV8.png Concurrency in Python - concepts, frameworks and best practices https://i.ytimg.com/vi/Do7JtnPh1Mg/maxresdefault.jpg Concurrency in Python - concepts, frameworks and best practices This talk discusses: - concurrency concepts (e. g. atomicity, race conditions and deadlocks) - frameworks for concurrency and their use (`threading`, `multiprocessing`, `concurrent.futures`, `asyncio`) - higher abstractions, e. g. queues and active objects - best practices for writing concurrent code https://www.youtube.com/watch?v=Do7JtnPh1Mg https://www.youtube-nocookie.com/embed/Do7JtnPh1Mg 2018-11-05 yes https://pycon.de/archive/2018/talks/creating-an-inclusive-corporate-culture/ 2018-11-05 https://pycon.de/static/media/social/talks/78ANWT.png Creating an inclusive corporate culture https://i.ytimg.com/vi/nwyiiB1NK3w/maxresdefault.jpg Creating an inclusive corporate culture Having a tech career as a minority is challenging. It could mean being the only one to speak against the popular opinion, or becoming more visible to get the same level of recognition. What can we do on the corporate level to make sure everyone feels welcome and retain these talents? Creating an inclusive corporate culture helps us achieve just that. This talk shares concrete steps that employees and employers can take to improve minorities in tech’s sense of belonging and engagement. https://www.youtube.com/watch?v=nwyiiB1NK3w https://www.youtube-nocookie.com/embed/nwyiiB1NK3w 2018-11-05 yes https://pycon.de/archive/2018/talks/cython-to-speed-up-your-python-code/ 2018-11-05 https://pycon.de/static/media/social/talks/7ZNCP8.png Cython to speed up your Python code https://i.ytimg.com/vi/zx0wMxuh-wk/maxresdefault.jpg Cython to speed up your Python code Come and learn how to speed up and optimise your Python code with the Cython compiler. https://www.youtube.com/watch?v=zx0wMxuh-wk https://www.youtube-nocookie.com/embed/zx0wMxuh-wk 2018-11-05 yes https://pycon.de/archive/2018/talks/data-science-complexity-and-solutions-in-real-industrial-projects/ 2018-11-05 https://pycon.de/static/media/social/talks/EZC8FK.png Data science complexity and solutions in real industrial projects https://i.ytimg.com/vi/bmtGr0LSb_Y/maxresdefault.jpg Data science complexity and solutions in real industrial projects Due to the complexity associated with data, using machine learning in real-world scenarios is difficult. I’d like to give an insight into how we tackle this task based on examples of real projects in an industrial environment. https://www.youtube.com/watch?v=bmtGr0LSb_Y https://www.youtube-nocookie.com/embed/bmtGr0LSb_Y 2018-11-05 yes https://pycon.de/archive/2018/talks/data-science-meets-data-protection-keeping-your-data-secure-while-learning-from-it/ 2018-05-27 https://pycon.de/static/media/social/talks/PYBGWL.png Data Science meets Data Protection: Keeping your data secure while learning from it. https://pycon.de/archive/2018/talks/deep-learning-with-pytorch-for-more-fun-and-profit-part-ii/ 2018-11-05 https://pycon.de/static/media/social/talks/DPYMZZ.png Deep Learning with PyTorch for more Fun and Profit (Part II) https://i.ytimg.com/vi/yB1rUfPILFY/maxresdefault.jpg Deep Learning with PyTorch for more Fun and Profit (Part II) There are all these great articles and blog posts about Deep Learning describing all that awesome stuff. - Is it all that easy? Let's check! We'll look into: style transfer (making a picture look like painting), speech generation (like Siri or Alexa) and text generation (writing a story). In this talk I'll describe the whole journey: A fun ride from the idea to the very end including all the struggles, failures and successes. https://www.youtube.com/watch?v=yB1rUfPILFY https://www.youtube-nocookie.com/embed/yB1rUfPILFY 2018-11-05 yes https://pycon.de/archive/2018/talks/designing-better-drugs-with-machine-learning/ 2018-06-03 https://pycon.de/static/media/social/talks/FVLSNE.png Designing better drugs with machine learning https://pycon.de/archive/2018/talks/designing-restful-apis/ 2018-05-22 https://pycon.de/static/media/social/talks/YKNWUB.png Designing RESTful APIs https://pycon.de/archive/2018/talks/developing-ecommerce-platform-with-django-oscar/ 2018-11-05 https://pycon.de/static/media/social/talks/EVT9PV.png Developing ecommerce platform with Django Oscar https://i.ytimg.com/vi/7b8r1eioNPY/maxresdefault.jpg Developing ecommerce platform with Django Oscar In this talk I’m going to describe from A to Z, how using Django and Django Oscar design complex modern ecommerce platform to buy, sell, supply and exchange goods. https://www.youtube.com/watch?v=7b8r1eioNPY https://www.youtube-nocookie.com/embed/7b8r1eioNPY 2018-11-05 yes https://pycon.de/archive/2018/talks/distributed-hyperparameter-search-with-sklearn-and-kubernetes/ 2018-11-05 https://pycon.de/static/media/social/talks/PA9FZK.png Distributed Hyperparameter search with sklearn and kubernetes https://i.ytimg.com/vi/blUc9w3QLuA/maxresdefault.jpg Distributed Hyperparameter search with sklearn and kubernetes In this talk, I will show how you can harness the scheduling of kubernetes for distributing hyperparameter search with sklearn onto a cluster of nodes. This can be achieved quite easily and with just a few changes to the original code, so the Data Scientist won't be bothered by complex kubernetes internals. https://www.youtube.com/watch?v=blUc9w3QLuA https://www.youtube-nocookie.com/embed/blUc9w3QLuA 2018-11-05 yes https://pycon.de/archive/2018/talks/driving-simulation-and-data-analysis-of-magnetic-nanostructures-through-jupyter-notebook/ 2018-05-25 https://pycon.de/static/media/social/talks/3N7DNH.png Driving simulation and data analysis of magnetic nanostructures through Jupyter Notebook https://pycon.de/archive/2018/talks/enabling-the-chip-technologies-of-tomorrow-how-python-helps-us/ 2018-11-05 https://pycon.de/static/media/social/talks/EC3UJW.png Enabling the chip technologies of tomorrow – how Python helps us https://i.ytimg.com/vi/XWmJlTIJ7OA/maxresdefault.jpg Enabling the chip technologies of tomorrow – how Python helps us With the upraise of data science, more and more People from a non-programming background come to Python. We'd like to share our experience how we successfully use Python in a non-software development department. https://www.youtube.com/watch?v=XWmJlTIJ7OA https://www.youtube-nocookie.com/embed/XWmJlTIJ7OA 2018-11-05 yes https://pycon.de/archive/2018/talks/experiences-from-applying-convolutional-neural-networks-for-classifying-2d-sensor-data/ 2018-05-25 https://pycon.de/static/media/social/talks/TEDNFL.png Experiences from applying Convolutional Neural Networks for classifying 2D sensor data https://pycon.de/archive/2018/talks/finance-sales-and-business-development-for-start-ups/ 2018-05-29 https://pycon.de/static/media/social/talks/VBC9GK.png Finance, Sales and Business Development for Start-Ups https://pycon.de/archive/2018/talks/from-exploration-to-deployment-combining-pytorch-and-tensorflow-for-deep-learning/ 2018-05-28 https://pycon.de/static/media/social/talks/WESP9T.png From exploration to deployment - combining PyTorch and TensorFlow for Deep Learning https://pycon.de/archive/2018/talks/from-wittgenstein-to-tensorflow-the-role-of-domain-specific-languages-and-language-design-in-machine-learning/ 2018-10-15 https://pycon.de/static/media/social/talks/L8NSBN.png From Wittgenstein to TensorFlow: The role of Domain Specific Languages and Language Design in Machine Learning https://pycon.de/archive/2018/talks/fulfilling-apache-arrow-s-promises-pandas-on-jvm-memory-without-a-copy/ 2018-11-05 https://pycon.de/static/media/social/talks/PSG873.png Fulfilling Apache Arrow's Promises: Pandas on JVM memory without a copy https://i.ytimg.com/vi/xW7IOaQvDsU/maxresdefault.jpg Fulfilling Apache Arrow's Promises: Pandas on JVM memory without a copy Apache Arrow's promise was to reduce the (serialization & copy) overhead of working with columnar data between different systems. Using the latest Pandas release and Arrow's ability to share memory between the JVM and Python as ingredients, we demonstrate that Arrow can fulfill this bold statement. The performance benefits of this will be shown using a typical data engineering use-case that produces data in the JVM and then passes it on to a Python-based machine learning model. https://www.youtube.com/watch?v=xW7IOaQvDsU https://www.youtube-nocookie.com/embed/xW7IOaQvDsU 2018-11-05 yes https://pycon.de/archive/2018/talks/germany-s-next-topic-model/ 2018-11-05 https://pycon.de/static/media/social/talks/TMW9U3.png Germany's next topic model https://i.ytimg.com/vi/sI7VpFNiy_I/maxresdefault.jpg Germany's next topic model The aim of this talk is to present a novel way of detecting topics that is especially suited for user generated content where topics are not as clearly separated as in the typical examples of Wikipedia or newsgroup articles. The basic idea is to compute a contextual similarity score that defines a network from which we can identify clusters through community detection. https://www.youtube.com/watch?v=sI7VpFNiy_I https://www.youtube-nocookie.com/embed/sI7VpFNiy_I 2018-11-05 yes https://pycon.de/archive/2018/talks/grammar-of-graphics-in-python/ 2018-11-05 https://pycon.de/static/media/social/talks/3D3BDP.png Grammar of Graphics in Python https://i.ytimg.com/vi/SNaWwk_HzK0/maxresdefault.jpg Grammar of Graphics in Python This talk will give an introduction to the concepts behind the grammar of graphics and how to use them in Python by means of the altair library and the vega specification. https://www.youtube.com/watch?v=SNaWwk_HzK0 https://www.youtube-nocookie.com/embed/SNaWwk_HzK0 2018-11-05 yes https://pycon.de/archive/2018/talks/how-to-develop-your-project-from-an-idea-to-architecture-design-in-50-minutes/ 2018-05-25 https://pycon.de/static/media/social/talks/C9H7FH.png How to develop your project from an idea to architecture design in 50 minutes https://pycon.de/archive/2018/talks/how-to-make-your-digital-communication-strong-future-ready/ 2018-06-01 https://pycon.de/static/media/social/talks/XGUAE3.png How to make your (digital) Communication strong & future ready https://pycon.de/archive/2018/talks/how-to-teach-space-invaders-to-your-computer/ 2018-11-05 https://pycon.de/static/media/social/talks/3ZBCVR.png How to teach space invaders to your computer https://i.ytimg.com/vi/2KfyvrEn8p8/maxresdefault.jpg How to teach space invaders to your computer A very brief introduction to reinforcement learning theory followed by a hands on section in which it is demonstrated how to train an algorithm to play space invaders. After this talk you should now what reinforcement learning is and where to dig deeper if you like it. https://www.youtube.com/watch?v=2KfyvrEn8p8 https://www.youtube-nocookie.com/embed/2KfyvrEn8p8 2018-11-05 yes https://pycon.de/archive/2018/talks/how-type-annotations-make-your-code-better/ 2018-11-05 https://pycon.de/static/media/social/talks/XX8KJD.png How type annotations make your code better https://i.ytimg.com/vi/hHxZkdDA-l0/maxresdefault.jpg How type annotations make your code better The real world examples on how type annotations make your code better and less complex for other developers. https://www.youtube.com/watch?v=hHxZkdDA-l0 https://www.youtube-nocookie.com/embed/hHxZkdDA-l0 2018-11-05 yes https://pycon.de/archive/2018/talks/interactive-visualization-of-traffic-data-using-bokeh/ 2018-11-05 https://pycon.de/static/media/social/talks/8Y7JHD.png Interactive Visualization of Traffic Data using Bokeh https://i.ytimg.com/vi/GOE__moR0eo/maxresdefault.jpg Interactive Visualization of Traffic Data using Bokeh How can you use Python to create an interactive and comprehensive visualization of Traffic or other GIS Data on a map? In this talk, we will show how one can use the tools of the Python Data Analysis and Visualization landscape to achieve this goal. https://www.youtube.com/watch?v=GOE__moR0eo https://www.youtube-nocookie.com/embed/GOE__moR0eo 2018-11-05 yes https://pycon.de/archive/2018/talks/introduction-and-practical-experience-about-quantum-computing-using-the-python-libraries-from-ibm-and-google/ 2018-05-25 https://pycon.de/static/media/social/talks/KF3BUL.png Introduction and practical experience about Quantum Computing using the Python libraries from IBM and Google https://pycon.de/archive/2018/talks/introduction-to-docker-for-pythonistas/ 2018-11-05 https://pycon.de/static/media/social/talks/SP8TP9.png Introduction to Docker for Pythonistas https://i.ytimg.com/vi/Fqq6F68SQFY/maxresdefault.jpg Introduction to Docker for Pythonistas Docker is a major driver of container virtualization and comes in very handy for your day-to-day work with Python. https://www.youtube.com/watch?v=Fqq6F68SQFY https://www.youtube-nocookie.com/embed/Fqq6F68SQFY 2018-11-05 yes https://pycon.de/archive/2018/talks/iot-using-python-on-linux-lessons-learned/ 2018-11-05 https://pycon.de/static/media/social/talks/NTCBZX.png IoT using Python on Linux: Lessons Learned https://i.ytimg.com/vi/M4cmKMxvYKE/maxresdefault.jpg IoT using Python on Linux: Lessons Learned This talk is about our experience using Python on a Linux based IoT device, the problems we were facing and the lessons we learned. An appliance using Bluetooth Low Energy, D-Bus, NetworkManager and a proprietary sensor interface - What could possibly go wrong? https://www.youtube.com/watch?v=M4cmKMxvYKE https://www.youtube-nocookie.com/embed/M4cmKMxvYKE 2018-11-05 yes https://pycon.de/archive/2018/talks/karabo-a-control-framework-fueled-by-python-asyncio/ 2018-05-27 https://pycon.de/static/media/social/talks/NC7Z3X.png Karabo - A control framework fueled by Python asyncio https://pycon.de/archive/2018/talks/machine-learning-as-a-service-how-to-deploy-ml-models-as-apis-without-going-nuts/ 2018-05-22 https://pycon.de/static/media/social/talks/RR9WTS.png Machine Learning as a Service: How to deploy ML Models as APIs without going nuts https://pycon.de/archive/2018/talks/measuring-the-hay-in-the-haystack-quantifying-hidden-variables-using-bayesian-inference/ 2018-06-04 https://pycon.de/static/media/social/talks/NP9PRT.png Measuring the hay in the haystack: quantifying hidden variables using Bayesian Inference https://pycon.de/archive/2018/talks/modern-asynchronous-programming/ 2018-05-27 https://pycon.de/static/media/social/talks/YGB9LX.png Modern asynchronous programming https://pycon.de/archive/2018/talks/observe-all-your-applications/ 2018-11-05 https://pycon.de/static/media/social/talks/XDLE7M.png Observe all your applications https://i.ytimg.com/vi/F6-nvgnlr9Y/maxresdefault.jpg Observe all your applications This talk will give you an overview how we monitor our full stack from the 2000 physical machines up to the 10,000 parallel running Python application processes, micro-service instances and batch processing jobs. https://www.youtube.com/watch?v=F6-nvgnlr9Y https://www.youtube-nocookie.com/embed/F6-nvgnlr9Y 2018-11-05 yes https://pycon.de/archive/2018/talks/pandas-io-tools-reading-and-writing-dataframes-as-files-and-databases/ 2018-05-25 https://pycon.de/static/media/social/talks/BQSZ3X.png Pandas IO Tools: Reading and Writing DataFrames as Files and Databases https://pycon.de/archive/2018/talks/performance-evaluation-of-gans-in-a-semi-supervised-ocr-use-case/ 2018-05-23 https://pycon.de/static/media/social/talks/SVALYW.png Performance evaluation of GANs in a semi-supervised OCR use case https://pycon.de/archive/2018/talks/processing-geodata-using-python/ 2018-11-05 https://pycon.de/static/media/social/talks/JPCMQV.png Processing Geodata using Python https://i.ytimg.com/vi/49QwoR2aG74/maxresdefault.jpg Processing Geodata using Python In this talk it is shown how to analyze, manipulate and visualize geospatial data using Python and the Jupyter Notebook. https://www.youtube.com/watch?v=49QwoR2aG74 https://www.youtube-nocookie.com/embed/49QwoR2aG74 2018-11-05 yes https://pycon.de/archive/2018/talks/productionizing-your-ml-code-seamlessly/ 2018-11-05 https://pycon.de/static/media/social/talks/US3HQJ.png Productionizing your ML code seamlessly https://i.ytimg.com/vi/M0A8GaT5qns/maxresdefault.jpg Productionizing your ML code seamlessly Nowadays, it's easy to build a model and play with data in a notebook, but hard to bring the code to production. This talk will aim to answer: 1. What does running an ML model in production involve? 2. How to improve your development workflow to make the path to production easier? https://www.youtube.com/watch?v=M0A8GaT5qns https://www.youtube-nocookie.com/embed/M0A8GaT5qns 2018-11-05 yes https://pycon.de/archive/2018/talks/prototyping-to-tested-code/ 2018-11-05 https://pycon.de/static/media/social/talks/ELAHUR.png Prototyping to tested code https://i.ytimg.com/vi/8fg7AkvG-Oc/maxresdefault.jpg Prototyping to tested code Developing prototypes and their tests both in Jupyter notebooks. https://www.youtube.com/watch?v=8fg7AkvG-Oc https://www.youtube-nocookie.com/embed/8fg7AkvG-Oc 2018-11-05 yes https://pycon.de/archive/2018/talks/put-your-data-on-a-map/ 2018-11-05 https://pycon.de/static/media/social/talks/J9NSJE.png Put your data on a map https://i.ytimg.com/vi/CdgI-TUv5z0/maxresdefault.jpg Put your data on a map Quick overview of tools to render your geo data on a map from jupyter notebook with examples. https://www.youtube.com/watch?v=CdgI-TUv5z0 https://www.youtube-nocookie.com/embed/CdgI-TUv5z0 2018-11-05 yes https://pycon.de/archive/2018/talks/pyccel-a-fortran-static-compiler-for-scientific-high-performance-computing/ 2018-05-26 https://pycon.de/static/media/social/talks/HSVZUS.png Pyccel, a Fortran static compiler for scientific High-Performance Computing https://pycon.de/archive/2018/talks/pychipio-a-multi-platform-pure-python-iot-server-library/ 2018-05-25 https://pycon.de/static/media/social/talks/QWCBVT.png PyChipIO - a multi-platform pure Python IoT Server library https://pycon.de/archive/2018/talks/python-birdies-codegolfing-for-better-understanding-and-fun/ 2018-11-05 https://pycon.de/static/media/social/talks/K7VAR3.png Python Birdies: Codegolfing for better understanding (and fun) https://i.ytimg.com/vi/DPKSSxtVXuY/maxresdefault.jpg Python Birdies: Codegolfing for better understanding (and fun) Codegolfing, the art of condensing ones code, seems counterproductive to good programming standards. In this talk, I will argue, with the use of examples, that this need not be the case, that playing with a language can give you better understanding of its internals, and in turn make you a better programmer. https://www.youtube.com/watch?v=DPKSSxtVXuY https://www.youtube-nocookie.com/embed/DPKSSxtVXuY 2018-11-05 yes https://pycon.de/archive/2018/talks/python-decorators-gift-or-poison/ 2018-11-05 https://pycon.de/static/media/social/talks/3EV7WB.png Python Decorators: Gift or Poison? https://i.ytimg.com/vi/VEexfP68LJs/maxresdefault.jpg Python Decorators: Gift or Poison? The talk will explain basics of Python Decorators, will show lots of examples and use cases, as well as for a beginner, as for an experienced developer. https://www.youtube.com/watch?v=VEexfP68LJs https://www.youtube-nocookie.com/embed/VEexfP68LJs 2018-11-05 yes https://pycon.de/archive/2018/talks/python-dependency-management/ 2018-11-05 https://pycon.de/static/media/social/talks/YMMKKW.png Python Dependency Management https://i.ytimg.com/vi/pA4XriRWVxQ/maxresdefault.jpg Python Dependency Management While `there should be one-- and preferably only one --obvious way to do it`, there are multiple for managing Python dependencies. Let's have a look at the current state of dependency management in Python and some of the tools available. https://www.youtube.com/watch?v=pA4XriRWVxQ https://www.youtube-nocookie.com/embed/pA4XriRWVxQ 2018-11-05 yes https://pycon.de/archive/2018/talks/python-on-the-blockchain-triumphs-and-tribulations-in-a-crypto-startup/ 2018-05-25 https://pycon.de/static/media/social/talks/VJ7SY7.png Python on the blockchain: Triumphs and tribulations in a crypto startup https://pycon.de/archive/2018/talks/python-with-and-without-pants/ 2018-11-05 https://pycon.de/static/media/social/talks/ASWMKL.png Python with and without Pants https://i.ytimg.com/vi/6T8MjdWmVaQ/maxresdefault.jpg Python with and without Pants This is a talk about building, packaging, and deploying Python code with Pants and PEX (https://www.pantsbuild.org). https://www.youtube.com/watch?v=6T8MjdWmVaQ https://www.youtube-nocookie.com/embed/6T8MjdWmVaQ 2018-11-05 yes https://pycon.de/archive/2018/talks/pytorch-as-a-scientific-computing-library-past-present-and-future/ 2018-11-05 https://pycon.de/static/media/social/talks/Z7FXM3.png PyTorch as a scientific computing library: past, present and future https://i.ytimg.com/vi/0Y4kY6PnYfM/maxresdefault.jpg PyTorch as a scientific computing library: past, present and future Python is very well known for its ecosystem of mature scientific computing packages. Despite that, the rapidly rising popularity of deep learning resulted in creation of a number of new libraries, including PyTorch. Although originally they were meant to provide better support for those domain specific use cases, one can come to a conclusion, that they can actually have wider applications. https://www.youtube.com/watch?v=0Y4kY6PnYfM https://www.youtube-nocookie.com/embed/0Y4kY6PnYfM 2018-11-05 yes https://pycon.de/archive/2018/talks/reproducibility-and-selection-bias-in-machine-learning/ 2018-11-05 https://pycon.de/static/media/social/talks/TUMAN8.png Reproducibility, and Selection Bias in Machine Learning https://i.ytimg.com/vi/MOBs6MNepDk/maxresdefault.jpg Reproducibility, and Selection Bias in Machine Learning In this talk I will provide a solid introduction to the topics of reproducibility and selection bias, with examples taken from the biomedical research, in which reliability of estimator models is paramount. https://www.youtube.com/watch?v=MOBs6MNepDk https://www.youtube-nocookie.com/embed/MOBs6MNepDk 2018-11-05 yes https://pycon.de/archive/2018/talks/reticulate-r-interface-to-python/ 2018-11-05 https://pycon.de/static/media/social/talks/PGWRFX.png reticulate: R interface to Python https://i.ytimg.com/vi/EJxQSa9lwfM/maxresdefault.jpg reticulate: R interface to Python This talk presents the R package reticulate introduced by RStudio in 2018. It allows to import Python modules, source scripts, convert and manipulate objects and use a Python repl in R. https://www.youtube.com/watch?v=EJxQSa9lwfM https://www.youtube-nocookie.com/embed/EJxQSa9lwfM 2018-11-05 yes https://pycon.de/archive/2018/talks/rpackutils/ 2018-05-15 https://pycon.de/static/media/social/talks/PFWFJM.png RPackUtils https://pycon.de/archive/2018/talks/satellite-data-is-for-everyone-insights-into-modern-remote-sensing-research-with-open-data-and-python/ 2018-05-30 https://pycon.de/static/media/social/talks/TTULUU.png Satellite data is for everyone: insights into modern remote sensing research with open data and Python https://pycon.de/archive/2018/talks/satellite-image-segmentation-photovoltaic-potential-estimation/ 2018-11-05 https://pycon.de/static/media/social/talks/WSMVVU.png Satellite Image Segmentation Photovoltaic Potential Estimation https://i.ytimg.com/vi/SoJuk08u0DI/maxresdefault.jpg Satellite Image Segmentation Photovoltaic Potential Estimation Using Google Cloud Machine Learning Engine to built a model that help in estimating photovoltaic system design. An end-to-end python based deep learning application. https://www.youtube.com/watch?v=SoJuk08u0DI https://www.youtube-nocookie.com/embed/SoJuk08u0DI 2018-11-05 yes https://pycon.de/archive/2018/talks/scalable-scientific-computing-using-dask/ 2018-11-05 https://pycon.de/static/media/social/talks/QFNS3Z.png Scalable Scientific Computing using Dask https://i.ytimg.com/vi/OhstDq8l3OM/maxresdefault.jpg Scalable Scientific Computing using Dask Pandas and NumPy are great tools to dive through data, do analysis and train machine learning models. They provide intuitive APIs and superb performance. Sadly they are both restricted to the main memory of a single machine and mostly also to a single CPU. Dask is a flexible tools for parallelizing NumPy and Pandas code on a single machine or a cluster. https://www.youtube.com/watch?v=OhstDq8l3OM https://www.youtube-nocookie.com/embed/OhstDq8l3OM 2018-11-05 yes https://pycon.de/archive/2018/talks/script-library-or-executable-you-can-have-it-all/ 2018-11-05 https://pycon.de/static/media/social/talks/SFSLZG.png Script, Library, or Executable: You can have it all! https://i.ytimg.com/vi/7jpM1Iw3OKk/maxresdefault.jpg Script, Library, or Executable: You can have it all! Sometimes a stand-alone script contains useful code to share across projects so you refactor into an importable library. After that library gains traction some less tech-savvy users want the functionality in a GUI. It can be difficult for beginner or intermediate Python developers to structure a Python package that can provide a good interface to CLI users, developers, and GUI lovers. This talk will describe one potential project layout, API guidelines, and tools to easily grow a project to support all of these. https://www.youtube.com/watch?v=7jpM1Iw3OKk https://www.youtube-nocookie.com/embed/7jpM1Iw3OKk 2018-11-05 yes https://pycon.de/archive/2018/talks/selinon-dynamic-distributed-task-flows/ 2018-11-05 https://pycon.de/static/media/social/talks/KPY88U.png Selinon - dynamic distributed task flows https://i.ytimg.com/vi/UDr9Lp_0rp0/maxresdefault.jpg Selinon - dynamic distributed task flows Selinon is a task flow manager that enhances Celery and gives you an ability to create advanced task flows serving large workflows in your cluster. https://www.youtube.com/watch?v=UDr9Lp_0rp0 https://www.youtube-nocookie.com/embed/UDr9Lp_0rp0 2018-11-05 yes https://pycon.de/archive/2018/talks/solving-data-science-problems-using-a-jupyter-notebook-and-sap-hana-s-in-database-machine-learning-libraries/ 2018-06-01 https://pycon.de/static/media/social/talks/HDMT9B.png Solving Data Science Problems using a Jupyter Notebook and SAP HANA's in-database Machine Learning Libraries https://pycon.de/archive/2018/talks/stretchy-nosql-database-behind-rest-api/ 2018-11-05 https://pycon.de/static/media/social/talks/GFFGSG.png Stretchy - NoSQL Database behind REST API https://i.ytimg.com/vi/FzXejIzMQ30/maxresdefault.jpg Stretchy - NoSQL Database behind REST API A dynamic REST API database. Think of "elasticsearch", but brutally simple. https://www.youtube.com/watch?v=FzXejIzMQ30 https://www.youtube-nocookie.com/embed/FzXejIzMQ30 2018-11-05 yes https://pycon.de/archive/2018/talks/strongly-typed-datasets-in-a-weakly-typed-world/ 2018-11-05 https://pycon.de/static/media/social/talks/TTBMDL.png Strongly typed datasets in a weakly typed world https://i.ytimg.com/vi/wtpQTWROPc0/maxresdefault.jpg Strongly typed datasets in a weakly typed world Strongly typed Parquet Datasets / Hive Tables are often used to exchange and preserve data in a Pandas-driven environment, where types are rather unstable. This results in multiple issues and these as well as potential solutions will be presented, together with an RFC directed to the community. https://www.youtube.com/watch?v=wtpQTWROPc0 https://www.youtube-nocookie.com/embed/wtpQTWROPc0 2018-11-05 yes https://pycon.de/archive/2018/talks/suggestions-from-python-and-solr/ 2018-11-05 https://pycon.de/static/media/social/talks/8DL88C.png Suggestions from Python and Solr https://i.ytimg.com/vi/Z17JLncewyo/maxresdefault.jpg Suggestions from Python and Solr Trying to guess what a user wants when she's typing something into the search box of our price comparison website is a surprisingly complex endevour. Our solution is based on the Solr SuggestComponent, heavily fortified with Python logic. https://www.youtube.com/watch?v=Z17JLncewyo https://www.youtube-nocookie.com/embed/Z17JLncewyo 2018-11-05 yes https://pycon.de/archive/2018/talks/testing-in-python-the-big-picture/ 2018-11-05 https://pycon.de/static/media/social/talks/M8KBBJ.png Testing in Python - The Big Picture https://i.ytimg.com/vi/8JtLzYdXba0/maxresdefault.jpg Testing in Python - The Big Picture Software testing is of course very important when it comes to quality assurance. But good testing strategies can also make our lives as developers easier. In this talk, we will take a look at different aspects of software testing and find out what the Python ecosystem has to offer for our testing needs. https://www.youtube.com/watch?v=8JtLzYdXba0 https://www.youtube-nocookie.com/embed/8JtLzYdXba0 2018-11-05 yes https://pycon.de/archive/2018/talks/tickling-not-too-thick-ticks/ 2018-05-28 https://pycon.de/static/media/social/talks/TK7KCG.png Tickling not too thick ticks! https://pycon.de/archive/2018/talks/understanding-neural-networks-by-playing-games/ 2018-05-29 https://pycon.de/static/media/social/talks/9P7C3X.png Understanding Neural Networks by Playing Games https://pycon.de/archive/2018/talks/what-s-new-in-python-3-7/ 2018-11-05 https://pycon.de/static/media/social/talks/LRM8PG.png What's new in Python 3.7? https://i.ytimg.com/vi/hnjX858YhFk/maxresdefault.jpg What's new in Python 3.7? Scheduled for release in mid-June after the conference, Python 3.7 is shaping up to be a feature-packed release! This talk will cover all the new features of note that will be making their debut in Python 3.7. https://www.youtube.com/watch?v=hnjX858YhFk https://www.youtube-nocookie.com/embed/hnjX858YhFk 2018-11-05 yes https://pycon.de/archive/2018/talks/where-the-heck-is-my-memory/ 2018-11-05 https://pycon.de/static/media/social/talks/YV9WJ3.png Where the heck is my memory? https://i.ytimg.com/vi/C-1XWTgFo5g/maxresdefault.jpg Where the heck is my memory? In this talk I want to introduce you to python memory management and want to convince you that it is worth knowing a few details about it even while writing pure python code. In the end, I want you to leave with a better knowledge about what’s going on and equip you with a few tools and best practices to face the harsh world of memory management. https://www.youtube.com/watch?v=C-1XWTgFo5g https://www.youtube-nocookie.com/embed/C-1XWTgFo5g 2018-11-05 yes https://pycon.de/archive/2018/talks/write-your-own-decorators/ 2018-05-25 https://pycon.de/static/media/social/talks/NZJGSH.png Write your Own Decorators https://pycon.de/archive/2018/talks/your-first-nlp-project-peaks-and-pitfalls-of-unstructured-data/ 2018-11-05 https://pycon.de/static/media/social/talks/3HXB7K.png Your first NLP project: peaks and pitfalls of unstructured data https://i.ytimg.com/vi/bf2hISLgK84/maxresdefault.jpg Your first NLP project: peaks and pitfalls of unstructured data If you are looking for short, practical recipes for different natural language processing use cases in Python, this talk is for you! https://www.youtube.com/watch?v=bf2hISLgK84 https://www.youtube-nocookie.com/embed/bf2hISLgK84 2018-11-05 yes https://pycon.de/archive/2018/talks/zodb-the-graph-database-for-pythondevelopers/ 2018-11-05 https://pycon.de/static/media/social/talks/S3AXGV.png ZODB: The Graph Database for PythonDevelopers https://i.ytimg.com/vi/tcYyiqbUdKI/maxresdefault.jpg ZODB: The Graph Database for PythonDevelopers The ZODB is a mature graph database written in Python and optimized in C. Just subclass off of class Persistent Object, and Persistent Container, and your objects, graphs and applications become persistent. This talk teaches you what you need to know to start using a pythonic graph database. https://www.youtube.com/watch?v=tcYyiqbUdKI https://www.youtube-nocookie.com/embed/tcYyiqbUdKI 2018-11-05 yes https://pycon.de/archive/2019/talks/10-ways-to-debug-python-code/ 2019-12-16 https://pycon.de/static/media/social/talks/TRFD98.png 10 ways to debug Python code https://i.ytimg.com/vi/cokP4XAhcwo/maxresdefault.jpg 10 ways to debug Python code Are your debugging skills limited to "print", or do you sometimes think there must be a better way to figure out what's going on? I will show 10 ways to debug Python code, and share tips and tricks for effective debugging. https://www.youtube.com/watch?v=cokP4XAhcwo https://www.youtube-nocookie.com/embed/cokP4XAhcwo 2019-12-16 yes https://pycon.de/archive/2019/talks/6-years-of-docker-the-good-the-bad-and-python-packaging/ 2019-12-16 https://pycon.de/static/media/social/talks/A7FWAW.png 6 Years of Docker: The Good, the Bad and Python Packaging https://i.ytimg.com/vi/kgqdOftkZ-E/maxresdefault.jpg 6 Years of Docker: The Good, the Bad and Python Packaging Local development of python code inside a docker container is surprisingly broken. In this talk I will walk you through the proper setup of a local python development environment using docker. https://www.youtube.com/watch?v=kgqdOftkZ-E https://www.youtube-nocookie.com/embed/kgqdOftkZ-E 2019-12-16 yes https://pycon.de/archive/2019/talks/a-bayesian-workflow-with-pymc-and-arviz/ 2019-12-08 https://pycon.de/static/media/social/talks/RRLRYG.png A Bayesian Workflow with PyMC and ArviZ https://i.ytimg.com/vi/WbNmcvxRwow/maxresdefault.jpg A Bayesian Workflow with PyMC and ArviZ Bayesian Modelling has several advantages such as the handling of uncertainty. While the advantages are well known, implementing a Bayesian model can be a bit more involved and some care needs to be taken to check whether the model converged. https://www.youtube.com/watch?v=WbNmcvxRwow https://www.youtube-nocookie.com/embed/WbNmcvxRwow 2019-12-08 yes https://pycon.de/archive/2019/talks/a-medieval-dsl-parsing-heraldic-blazons-with-python/ 2019-12-16 https://pycon.de/static/media/social/talks/EE33M8.png A Medieval DSL? Parsing Heraldic Blazons with Python https://i.ytimg.com/vi/vjlKvIJyzQc/maxresdefault.jpg A Medieval DSL? Parsing Heraldic Blazons with Python Medieval people invented one of the first domain specific languages to describe how to paint Coats of Arms. We'll learn how to write our own parse grammar to parse this language, and then look at the parse grammar of Python itself! https://www.youtube.com/watch?v=vjlKvIJyzQc https://www.youtube-nocookie.com/embed/vjlKvIJyzQc 2019-12-16 yes https://pycon.de/archive/2019/talks/a-tour-of-jupyterlab-extensions/ 2019-12-08 https://pycon.de/static/media/social/talks/EH7FWD.png A Tour of JupyterLab Extensions https://i.ytimg.com/vi/3pdrzhny9Lc/maxresdefault.jpg A Tour of JupyterLab Extensions JupyterLab can be extended via third-party extensions written by developers from the Jupyter community. This is a tour of 20 of these extensions, in 20 minutes. Demos included! https://www.youtube.com/watch?v=3pdrzhny9Lc https://www.youtube-nocookie.com/embed/3pdrzhny9Lc 2019-12-08 yes https://pycon.de/archive/2019/talks/abridged-metaprogramming-classics-this-episode-pytest/ 2019-12-16 https://pycon.de/static/media/social/talks/XTD7TE.png Abridged metaprogramming classics - this episode: pytest https://i.ytimg.com/vi/zHpeMTJsBRk/maxresdefault.jpg Abridged metaprogramming classics - this episode: pytest This talk is not about how to use pytest. pytest is a good project to explore metaprogramming techniques like introspection and code as data in the context of solving real world problems. In this case: implementing a test framework. https://www.youtube.com/watch?v=zHpeMTJsBRk https://www.youtube-nocookie.com/embed/zHpeMTJsBRk 2019-12-16 yes https://pycon.de/archive/2019/talks/active-learning-with-bayesian-nonnegative-matrix-factorization-for-recommender-systems/ 2019-05-25 https://pycon.de/static/media/social/talks/ZBEEM3.png Active Learning with Bayesian Nonnegative Matrix Factorization for Recommender Systems https://pycon.de/archive/2019/talks/ai-intentions-and-code-completion/ 2019-12-08 https://pycon.de/static/media/social/talks/L998EU.png AI Intentions and Code Completion https://i.ytimg.com/vi/98nyBGXalNg/maxresdefault.jpg AI Intentions and Code Completion Datalore supports intentions – code suggestions based on what you’ve just written. They cover a wide range of situations from generating code to warnings and optimization suggestions. https://www.youtube.com/watch?v=98nyBGXalNg https://www.youtube-nocookie.com/embed/98nyBGXalNg 2019-12-08 yes https://pycon.de/archive/2019/talks/airflow-your-ally-for-automating-machine-learning-and-data-pipelines/ 2019-05-23 https://pycon.de/static/media/social/talks/7YG8RT.png Airflow: your ally for automating machine learning and data pipelines https://pycon.de/archive/2019/talks/algo-rules-how-do-we-get-the-ethics-into-the-code/ 2019-12-08 https://pycon.de/static/media/social/talks/ZEEMKS.png Algo.Rules - How do we get the ethics into the code? https://i.ytimg.com/vi/A07TlcpH428/maxresdefault.jpg Algo.Rules - How do we get the ethics into the code? In the keynote I will present our Algo.Rules, 9 rules for the design of algorithmic systems and address the questions “What standards of quality should algorithms be held to?” and “How can we make sure that these standards are actually being implemented?” https://www.youtube.com/watch?v=A07TlcpH428 https://www.youtube-nocookie.com/embed/A07TlcpH428 2019-12-08 yes https://pycon.de/archive/2019/talks/an-introduction-to-concurrency-and-parallelism-using-python-programming-language/ 2019-12-16 https://pycon.de/static/media/social/talks/SC7JGK.png An Introduction to Concurrency and Parallelism using Python Programming Language https://i.ytimg.com/vi/iEpkP-kkDJ0/maxresdefault.jpg An Introduction to Concurrency and Parallelism using Python Programming Language Python concurrency and parallelism concepts like Multiprocessing, Multithreading, Coroutine, Asynchronous I/O will be explained. We will learn to write simpler code with improved response time and throughput. https://www.youtube.com/watch?v=iEpkP-kkDJ0 https://www.youtube-nocookie.com/embed/iEpkP-kkDJ0 2019-12-16 yes https://pycon.de/archive/2019/talks/apache-airflow-for-beginners/ 2020-01-04 https://pycon.de/static/media/social/talks/93C7TF.png 🌈Apache Airflow for beginners https://i.ytimg.com/vi/YWtfU0MQZ_4/maxresdefault.jpg 🌈Apache Airflow for beginners This talk gives an introduction to *Apache Airflow*, that facilitates workflow automation and scheduling. You will learn about the core concepts in Airflow and how they fit together to form a data pipeline. https://www.youtube.com/watch?v=YWtfU0MQZ_4 https://www.youtube-nocookie.com/embed/YWtfU0MQZ_4 2020-01-04 yes https://pycon.de/archive/2019/talks/applying-deployment-oriented-mindset-for-building-machine-learning-models/ 2019-05-26 https://pycon.de/static/media/social/talks/LGC8XP.png Applying deployment oriented mindset for building Machine Learning models https://pycon.de/archive/2019/talks/are-you-sure-about-that-uncertainty-quantification-in-ai/ 2019-12-08 https://pycon.de/static/media/social/talks/WQMPSW.png Are you sure about that?! Uncertainty Quantification in AI https://i.ytimg.com/vi/LCDIqL-8bHs/maxresdefault.jpg Are you sure about that?! Uncertainty Quantification in AI There is a strong need in many AI applications to state the certainty about their predictions. This talk elaborates on different ways to perform uncertainty quantification in deep learning and classical methods. https://www.youtube.com/watch?v=LCDIqL-8bHs https://www.youtube-nocookie.com/embed/LCDIqL-8bHs 2019-12-08 yes https://pycon.de/archive/2019/talks/automated-feature-engineering-and-selection-in-python/ 2019-12-08 https://pycon.de/static/media/social/talks/Q7KLXV.png Automated Feature Engineering and Selection in Python https://i.ytimg.com/vi/4-4pKPv9lJ4/maxresdefault.jpg Automated Feature Engineering and Selection in Python Careful feature engineering and selection can be just as important as choosing the right ML model & hyperparameters. I will present several options for automating the feature engineering and selection process with a focus on the autofeat library. https://www.youtube.com/watch?v=4-4pKPv9lJ4 https://www.youtube-nocookie.com/embed/4-4pKPv9lJ4 2019-12-08 yes https://pycon.de/archive/2019/talks/automating-feature-engineering-for-supervised-learning-methods-open-source-tools-and-prospects/ 2019-05-02 https://pycon.de/static/media/social/talks/7EEPKX.png Automating feature engineering for supervised learning? Methods, open-source tools and prospects. https://pycon.de/archive/2019/talks/avoiding-ml-fobo/ 2019-12-08 https://pycon.de/static/media/social/talks/KC99NY.png Avoiding ML FOBO https://i.ytimg.com/vi/UxfGPZUAYls/maxresdefault.jpg Avoiding ML FOBO Everyday there is a new package or algorithm to use- it can be hard to determine what is useful and what is only hype. The speakers offer a practical roadmap and checklist to help you cut through the hype and focus on developing useful ML products. https://www.youtube.com/watch?v=UxfGPZUAYls https://www.youtube-nocookie.com/embed/UxfGPZUAYls 2019-12-08 yes https://pycon.de/archive/2019/talks/beyond-paradigms-a-new-key-to-grok-python-other-languages/ 2019-12-16 https://pycon.de/static/media/social/talks/QCNNTW.png Beyond Paradigms: a new key to grok Python & other languages https://i.ytimg.com/vi/bF3a2VYXxa0/maxresdefault.jpg Beyond Paradigms: a new key to grok Python & other languages Focus on features, not paradigms. This new approach to the study of programming languages offers practical advice for programmers learning a new language, adopting coding idioms, and choosing suitable design patterns. https://www.youtube.com/watch?v=bF3a2VYXxa0 https://www.youtube-nocookie.com/embed/bF3a2VYXxa0 2019-12-16 yes https://pycon.de/archive/2019/talks/birds-of-a-feather-flock-together-tracking-pigeons-with-python-and-opencv/ 2019-05-25 https://pycon.de/static/media/social/talks/T7EEEB.png Birds of a feather flock together - Tracking pigeons with Python and OpenCV https://pycon.de/archive/2019/talks/boosting-simulation-performance-with-python/ 2019-12-16 https://pycon.de/static/media/social/talks/CEYMMK.png Boosting simulation performance with Python https://i.ytimg.com/vi/-0TVMCTnCg4/maxresdefault.jpg Boosting simulation performance with Python In this talk I will present the architecture of our simulation (written in Python) which allows us to simulate hours of real-life in only minutes of simulation. I will describe challenges we encountered and how we handled them. https://www.youtube.com/watch?v=-0TVMCTnCg4 https://www.youtube-nocookie.com/embed/-0TVMCTnCg4 2019-12-16 yes https://pycon.de/archive/2019/talks/break-your-api-gently-or-not-at-all/ 2019-12-16 https://pycon.de/static/media/social/talks/QXB8DR.png Break your API gently - or not at all https://i.ytimg.com/vi/qDILVhNTuBA/maxresdefault.jpg Break your API gently - or not at all As hard as we try to write good code, there will always be cases in which we wish we had designed a different function signature, chosen another attribute name, ... But you can't change it anymore because it's public API - or can you? https://www.youtube.com/watch?v=qDILVhNTuBA https://www.youtube-nocookie.com/embed/qDILVhNTuBA 2019-12-16 yes https://pycon.de/archive/2019/talks/build-a-machine-learning-pipeline-with-jupyter-and-azure/ 2019-07-28 https://pycon.de/static/media/social/talks/TNHATE.png Build a Machine Learning pipeline with Jupyter and Azure https://pycon.de/archive/2019/talks/canceled-create-cuda-kernels-from-python-using-numba-and-cupy/ 2019-05-26 https://pycon.de/static/media/social/talks/8NNMCU.png CANCELED: Create CUDA kernels from Python using Numba and CuPy. https://pycon.de/archive/2019/talks/cancelled-first-steps-in-julia/ 2019-05-26 https://pycon.de/static/media/social/talks/M8MLZX.png CANCELLED: First steps in Julia https://pycon.de/archive/2019/talks/cancelled-fresh-new-pythonic-database-edgedb-and-why-it-s-the-future/ 2019-06-09 https://pycon.de/static/media/social/talks/SGVKVS.png CANCELLED: Fresh New Pythonic Database: EdgeDB (And Why It's the Future) https://pycon.de/archive/2019/talks/chips-made-from-python/ 2019-12-16 https://pycon.de/static/media/social/talks/WURZX8.png Chips Made From Python https://i.ytimg.com/vi/o3WkrKdGrRc/maxresdefault.jpg Chips Made From Python Introduction to Python *hardware description libraries*, and how they are being used to design modern silicon, including open-source RISC-V CPUs. https://www.youtube.com/watch?v=o3WkrKdGrRc https://www.youtube-nocookie.com/embed/o3WkrKdGrRc 2019-12-16 yes https://pycon.de/archive/2019/talks/commenting-code-beyond-common-wisdom/ 2019-12-16 https://pycon.de/static/media/social/talks/SUC9VW.png Commenting code — beyond common wisdom https://i.ytimg.com/vi/tP5uWCruaBs/maxresdefault.jpg Commenting code — beyond common wisdom Good code comments are important for software maintenance. This talk goes beyond the common wisdom you find in most books and online and explains when this common wisdom falls short. https://www.youtube.com/watch?v=tP5uWCruaBs https://www.youtube-nocookie.com/embed/tP5uWCruaBs 2019-12-16 yes https://pycon.de/archive/2019/talks/creating-an-interactive-ml-conference-showcase/ 2019-12-08 https://pycon.de/static/media/social/talks/MMXQUC.png Creating an Interactive ML Conference Showcase https://i.ytimg.com/vi/YyKQOKPK5Kg/maxresdefault.jpg Creating an Interactive ML Conference Showcase Our goal is to create a simple yet interactive showcase for computer vision using a Python notebook. In a trade fair setup, we want to learn new object classes quickly using very few training examples. Thus, we rely on pretrained neural networks for https://www.youtube.com/watch?v=YyKQOKPK5Kg https://www.youtube-nocookie.com/embed/YyKQOKPK5Kg 2019-12-08 yes https://pycon.de/archive/2019/talks/current-affairs-updates-and-the-roadmap-of-scikit-learn-and-scikit-learn-contrib/ 2019-06-07 https://pycon.de/static/media/social/talks/X7FSX9.png Current affairs, updates, and the roadmap of scikit-learn and scikit-learn-contrib https://pycon.de/archive/2019/talks/dash-interactive-data-visualization-web-apps-with-no-javascript/ 2019-12-08 https://pycon.de/static/media/social/talks/R7PELL.png Dash: Interactive Data Visualization Web Apps with no Javascript https://i.ytimg.com/vi/SqIgn5bz4lc/maxresdefault.jpg Dash: Interactive Data Visualization Web Apps with no Javascript Interactive web pages and visualizations with no JavaScript? What could go wrong? What you can, can't, should and probably shouldn't do with plotly/Dash. https://www.youtube.com/watch?v=SqIgn5bz4lc https://www.youtube-nocookie.com/embed/SqIgn5bz4lc 2019-12-08 yes https://pycon.de/archive/2019/talks/data-literacy-for-managers/ 2019-12-08 https://pycon.de/static/media/social/talks/WUEBKT.png Data Literacy for Managers https://i.ytimg.com/vi/t_7pZkPifqc/maxresdefault.jpg Data Literacy for Managers Artificial Intelligence need to be better understood in enterprises. Close the communications gap between engineers and management. Making data litteracy happen in your organisation. https://www.youtube.com/watch?v=t_7pZkPifqc https://www.youtube-nocookie.com/embed/t_7pZkPifqc 2019-12-08 yes https://pycon.de/archive/2019/talks/decentralized-and-privacy-preserving-ml-via-tensorflow-federated/ 2019-12-08 https://pycon.de/static/media/social/talks/WRPFUC.png Decentralized and Privacy-Preserving ML via TensorFlow Federated https://i.ytimg.com/vi/PP209iFnnH8/maxresdefault.jpg Decentralized and Privacy-Preserving ML via TensorFlow Federated Federated Learning is a technology to train machine learning models and run data analytics on decentralized data. This tutorial will demonstrate step-by-step how to train largescale TensorFlow models and custom computations in federated environments. https://www.youtube.com/watch?v=PP209iFnnH8 https://www.youtube-nocookie.com/embed/PP209iFnnH8 2019-12-08 yes https://pycon.de/archive/2019/talks/deep-learning-for-healthcare-with-pytorch/ 2019-06-09 https://pycon.de/static/media/social/talks/78RYGG.png Deep Learning for Healthcare with PyTorch https://pycon.de/archive/2019/talks/detecting-and-analyzing-solar-panels-in-switzerland-using-aerial-imagery/ 2019-05-26 https://pycon.de/static/media/social/talks/RJCW8U.png Detecting and Analyzing Solar Panels in Switzerland using Aerial Imagery https://pycon.de/archive/2019/talks/developers-vs-enterprise/ 2019-12-16 https://pycon.de/static/media/social/talks/XJLSWG.png Developers vs. Enterprise https://i.ytimg.com/vi/ojbrpJqgGn4/maxresdefault.jpg Developers vs. Enterprise This 30-minute talk will give you an overview about project management, success factors and specialties within enterprises. This will be a guide how you promote internal projects and bring them to success within a highly "political" environment. https://www.youtube.com/watch?v=ojbrpJqgGn4 https://www.youtube-nocookie.com/embed/ojbrpJqgGn4 2019-12-16 yes https://pycon.de/archive/2019/talks/docker-and-python-a-match-made-in-heaven/ 2019-12-16 https://pycon.de/static/media/social/talks/VERUFX.png Docker and Python - A Match made in Heaven https://i.ytimg.com/vi/Q2u1wcfmlzw/maxresdefault.jpg Docker and Python - A Match made in Heaven Containers have revolutionized the way we build and ship software. This talk is aimed at newcomers and people with limited experience with Docker and will cover the basics of Docker and how it can be used alongside Python. https://www.youtube.com/watch?v=Q2u1wcfmlzw https://www.youtube-nocookie.com/embed/Q2u1wcfmlzw 2019-12-16 yes https://pycon.de/archive/2019/talks/does-hate-sound-the-same-in-all-languages/ 2019-12-08 https://pycon.de/static/media/social/talks/QA3EVU.png Does hate sound the same in all languages? https://i.ytimg.com/vi/xFTTHCH4yiQ/maxresdefault.jpg Does hate sound the same in all languages? How might we make social media safer and more inclusive? Tackling hate speech online is not easy, especially if it’s in a language less circulated. This talk describes detecting hate speech in Romanian from dataset creation to hate speech model. https://www.youtube.com/watch?v=xFTTHCH4yiQ https://www.youtube-nocookie.com/embed/xFTTHCH4yiQ 2019-12-08 yes https://pycon.de/archive/2019/talks/dr-schmood-s-notebook-of-python-calisthenics-and-orthodontia/ 2019-12-16 https://pycon.de/static/media/social/talks/F3TCZN.png Dr. Schmood's Notebook of Python Calisthenics and Orthodontia https://i.ytimg.com/vi/BAsL_cbkQDc/maxresdefault.jpg Dr. Schmood's Notebook of Python Calisthenics and Orthodontia Explore the benefits of taking a functional approach when writing Python in Jupyter notebooks: reduce errors caused by out-of-order execution and hidden state while producing more readable code. https://www.youtube.com/watch?v=BAsL_cbkQDc https://www.youtube-nocookie.com/embed/BAsL_cbkQDc 2019-12-16 yes https://pycon.de/archive/2019/talks/driving-3d-printers-with-python-lessons-learned/ 2019-12-16 https://pycon.de/static/media/social/talks/JQAUEC.png Driving 3D Printers with Python: Lessons Learned https://i.ytimg.com/vi/9OcVmGbeh8A/maxresdefault.jpg Driving 3D Printers with Python: Lessons Learned OctoPrint is an open source web interface for 3D printers and deployed world wide on a large variety of devices. In this talk I explain some of the challenges in developing and maintaining such a piece of end user facing software in Python. https://www.youtube.com/watch?v=9OcVmGbeh8A https://www.youtube-nocookie.com/embed/9OcVmGbeh8A 2019-12-16 yes https://pycon.de/archive/2019/talks/embrace-uncertainty-why-to-go-beyond-point-estimators-for-valuable-ml-applications/ 2019-05-24 https://pycon.de/static/media/social/talks/QHNEFT.png Embrace uncertainty! Why to go beyond point estimators for valuable ML applications https://pycon.de/archive/2019/talks/equivariance-in-cnns-how-generalising-the-weight-sharing-property-increases-data-efficiency/ 2019-05-26 https://pycon.de/static/media/social/talks/RAS8UK.png Equivariance in CNNs: how generalising the weight-sharing property increases data-efficiency https://pycon.de/archive/2019/talks/event-sourced-story/ 2019-12-16 https://pycon.de/static/media/social/talks/SXSE8D.png Event-Sourced Story https://i.ytimg.com/vi/AdlmA6iHnfA/maxresdefault.jpg Event-Sourced Story After three years of utilizing event sourcing in [Growbots](http://growbots.com), I want to introduce to you its basics and share our experience acquired over that time - including complexity involved - so you can better assess whether it's a tool wo https://www.youtube.com/watch?v=AdlmA6iHnfA https://www.youtube-nocookie.com/embed/AdlmA6iHnfA 2019-12-16 yes https://pycon.de/archive/2019/talks/extended-ligthning-talks-cancelled-crunching-numbers-like-a-journalist/ 2019-08-26 https://pycon.de/static/media/social/talks/UPVJP7.png Extended Ligthning Talks CANCELLED: Crunching Numbers Like a Journalist https://pycon.de/archive/2019/talks/fairness-in-decision-making-with-ai-a-practical-guide-hands-on-tutorial-using-aequitas/ 2019-12-08 https://pycon.de/static/media/social/talks/FAYUEK.png Fairness in decision-making with AI: a practical guide & hands-on tutorial using Aequitas https://i.ytimg.com/vi/yOR71zBm3Uc/maxresdefault.jpg Fairness in decision-making with AI: a practical guide & hands-on tutorial using Aequitas In this tutorial, we are going to deep dive into algorithmic fairness, from metrics and definitions to practical case studies, including bias audits using Aequitas (http://github.com/dssg/aequitas) in real policy problems where AI is being used. https://www.youtube.com/watch?v=yOR71zBm3Uc https://www.youtube-nocookie.com/embed/yOR71zBm3Uc 2019-12-08 yes https://pycon.de/archive/2019/talks/fighting-fraud-finding-duplicates-at-scale/ 2019-12-08 https://pycon.de/static/media/social/talks/7PHMWA.png Fighting fraud: finding duplicates at scale https://i.ytimg.com/vi/hnDZGBuzKvo/maxresdefault.jpg Fighting fraud: finding duplicates at scale We present a duplicate detection system that we use to fight fraud in online classifieds. The system uses machine learning to analyze both text and images of 10 million ads daily and stop fraudulent listings before they do any harm. https://www.youtube.com/watch?v=hnDZGBuzKvo https://www.youtube-nocookie.com/embed/hnDZGBuzKvo 2019-12-08 yes https://pycon.de/archive/2019/talks/friend-or-foe-comparison-of-r-python-in-data-wrangling-visualisation/ 2019-05-26 https://pycon.de/static/media/social/talks/9D3JQC.png Friend or Foe: Comparison of R & Python in Data Wrangling & Visualisation https://pycon.de/archive/2019/talks/from-body-and-code-programming-in-times-of-acceptance/ 2019-12-16 https://pycon.de/static/media/social/talks/FQMFKX.png From body and code https://i.ytimg.com/vi/4Jzb4DwRTNE/maxresdefault.jpg From body and code This talk is meant to bring awareness about the need of diversity from theoretical to practical day by day actions. Diversity not only as social justice, but to reclaim knowledge proficiency, critical perspective and new ideas. https://www.youtube.com/watch?v=4Jzb4DwRTNE https://www.youtube-nocookie.com/embed/4Jzb4DwRTNE 2019-12-16 yes https://pycon.de/archive/2019/talks/gaussian-process-for-time-series-analysis/ 2019-12-08 https://pycon.de/static/media/social/talks/KNLNBB.png Gaussian Process for Time Series Analysis https://i.ytimg.com/vi/0p_6RzhSZEc/maxresdefault.jpg Gaussian Process for Time Series Analysis The aim of this talk is to introduce the notion of *Gaussian process* and describe how to use it to solve regressions problems and time series forecasting. https://www.youtube.com/watch?v=0p_6RzhSZEc https://www.youtube-nocookie.com/embed/0p_6RzhSZEc 2019-12-08 yes https://pycon.de/archive/2019/talks/gaussian-progress/ 2019-12-08 https://pycon.de/static/media/social/talks/WDAANU.png Gaussian Progress https://i.ytimg.com/vi/aICqoAG5BXQ/maxresdefault.jpg Gaussian Progress This talk is an attempt at explaining the power of the Gaussian[tm] by stepping up the ladder from Naive Bayes to Mixtures to Neural Mixtures to Gaussian Processes. https://www.youtube.com/watch?v=aICqoAG5BXQ https://www.youtube-nocookie.com/embed/aICqoAG5BXQ 2019-12-08 yes https://pycon.de/archive/2019/talks/get-to-grips-with-pandas-and-scikit-learn/ 2019-12-08 https://pycon.de/static/media/social/talks/9ZAA9T.png Get to grips with pandas and scikit-learn https://i.ytimg.com/vi/FfZlPDSeJ24/maxresdefault.jpg Get to grips with pandas and scikit-learn This session will be an exposition of data wrangling with pandas and machine learning with scikit-learn for Python Programmers. This hands-on workshop will cover a classification project, from importing the data to evaluating model performance. https://www.youtube.com/watch?v=FfZlPDSeJ24 https://www.youtube-nocookie.com/embed/FfZlPDSeJ24 2019-12-08 yes https://pycon.de/archive/2019/talks/getting-started-with-fpga-with-python/ 2019-12-16 https://pycon.de/static/media/social/talks/PHYCJL.png Getting started with FPGA with Python https://i.ytimg.com/vi/aTi35MMAVLs/maxresdefault.jpg Getting started with FPGA with Python In this review, we'll look into frameworks that will help Python developer start working with FPGA without prior knowledge of Verilog or VHDL. https://www.youtube.com/watch?v=aTi35MMAVLs https://www.youtube-nocookie.com/embed/aTi35MMAVLs 2019-12-16 yes https://pycon.de/archive/2019/talks/hidden-markov-models-for-chord-recognition-intuition-and-applications/ 2019-12-08 https://pycon.de/static/media/social/talks/YXNDB9.png Hidden Markov Models for Chord Recognition - Intuition and Applications https://i.ytimg.com/vi/t0mNFAFdz_Q/maxresdefault.jpg Hidden Markov Models for Chord Recognition - Intuition and Applications This tutorial describes the intuition behind Hidden Markov Models, with less mathematical formulas and with an application on Music Analytics - Chord Recognition https://www.youtube.com/watch?v=t0mNFAFdz_Q https://www.youtube-nocookie.com/embed/t0mNFAFdz_Q 2019-12-08 yes https://pycon.de/archive/2019/talks/hide-code-minimize-dependencies-boost-performance-the-pytorch-jit/ 2019-05-25 https://pycon.de/static/media/social/talks/8N37P9.png Hide Code, Minimize Dependencies, Boost Performance - The PyTorch JIT https://pycon.de/archive/2019/talks/how-micropython-went-into-space/ 2019-12-16 https://pycon.de/static/media/social/talks/7QRAWK.png How MicroPython went into space https://i.ytimg.com/vi/Vh_5Lz1mLq8/maxresdefault.jpg How MicroPython went into space MicroPython is a lean and efficient implementation of the Python 3 programming language, optimised to run on microcontrollers and in constrained environments. One of these environments is on a spacecraft. https://www.youtube.com/watch?v=Vh_5Lz1mLq8 https://www.youtube-nocookie.com/embed/Vh_5Lz1mLq8 2019-12-16 yes https://pycon.de/archive/2019/talks/how-strong-is-my-opponent-using-bayesian-methods-for-skill-assessment/ 2019-05-24 https://pycon.de/static/media/social/talks/3NUA9T.png How strong is my opponent? Using Bayesian methods for skill assessment https://pycon.de/archive/2019/talks/how-to-choose-better-colors-for-your-data-visualizations/ 2019-12-08 https://pycon.de/static/media/social/talks/QE3UUQ.png How to choose better colors for your data visualizations https://i.ytimg.com/vi/5kwlIuQArdU/maxresdefault.jpg How to choose better colors for your data visualizations Everybody is doing colorful charts with Python libraries such as matplotlib and bokeh but most people never change the basic configuration. This talk will teach you the basics of color theory to help you choose the right colors of your charts. https://www.youtube.com/watch?v=5kwlIuQArdU https://www.youtube-nocookie.com/embed/5kwlIuQArdU 2019-12-08 yes https://pycon.de/archive/2019/talks/how-to-write-tests-that-need-a-lot-of-data/ 2019-12-08 https://pycon.de/static/media/social/talks/P9ZZB3.png How to write tests that need a lot of data? https://i.ytimg.com/vi/zSkyxXpshj4/maxresdefault.jpg How to write tests that need a lot of data? In this talk Sander explains how you can write unit and integration tests that need a lot of data. As an example Sander shows how to test code of a warehouse management system (WMS). This is not about big-data, though. https://www.youtube.com/watch?v=zSkyxXpshj4 https://www.youtube-nocookie.com/embed/zSkyxXpshj4 2019-12-08 yes https://pycon.de/archive/2019/talks/interpretable-machine-learning-how-to-make-black-box-models-explainable/ 2019-05-04 https://pycon.de/static/media/social/talks/ZKXNTB.png Interpretable Machine Learning: How to make black box models explainable https://pycon.de/archive/2019/talks/introduction-to-automated-testing-with-pytest/ 2019-12-16 https://pycon.de/static/media/social/talks/3JUWNC.png Introduction to automated testing with pytest https://i.ytimg.com/vi/4R0dcsNrrAI/maxresdefault.jpg Introduction to automated testing with pytest We'll learn how to get started with developing automated tests in Python with the [pytest] test framework. [pytest]: https://github.com/pytest-dev/pytest https://www.youtube.com/watch?v=4R0dcsNrrAI https://www.youtube-nocookie.com/embed/4R0dcsNrrAI 2019-12-16 yes https://pycon.de/archive/2019/talks/is-it-me-or-the-gil/ 2019-12-16 https://pycon.de/static/media/social/talks/BHDEAR.png Is it me, or the GIL? https://i.ytimg.com/vi/-5bRSrCMyH0/maxresdefault.jpg Is it me, or the GIL? People refer Python's Global Interpreter Lock as main bottleneck for their performance critical applications. But how can you test if your application really suffer from the GIL? https://www.youtube.com/watch?v=-5bRSrCMyH0 https://www.youtube-nocookie.com/embed/-5bRSrCMyH0 2019-12-16 yes https://pycon.de/archive/2019/talks/job-panel/ 2019-10-06 https://pycon.de/static/media/social/talks/MEEGJC.png Job Panel https://pycon.de/archive/2019/talks/julia-for-python/ 2019-12-08 https://pycon.de/static/media/social/talks/ALEMND.png Julia for Python https://i.ytimg.com/vi/9cromzTumF4/maxresdefault.jpg Julia for Python Julia is a new Language, that is fast, high level, dynamic and optimized for Data Science. But due to its young age, it might not be for everyone yet. Learn about Julia's strengths and how you can integrate it in your Python workflow! https://www.youtube.com/watch?v=9cromzTumF4 https://www.youtube-nocookie.com/embed/9cromzTumF4 2019-12-08 yes https://pycon.de/archive/2019/talks/kartothek-table-management-for-cloud-object-stores-powered-by-apache-arrow-and-dask/ 2019-05-26 https://pycon.de/static/media/social/talks/CXYNHX.png Kartothek – Table management for cloud object stores powered by Apache Arrow and Dask https://pycon.de/archive/2019/talks/kubernetes-101-for-python-developers/ 2019-12-16 https://pycon.de/static/media/social/talks/D8VAN9.png Kubernetes 101 for Python Developers https://i.ytimg.com/vi/ior4nwIv3JQ/maxresdefault.jpg Kubernetes 101 for Python Developers `Kubernetes (K8s) is an open-source system for automating deployment, scaling, and management of containerized applications.` During this training you will learn how to use it and how it can help as a Python developer. https://www.youtube.com/watch?v=ior4nwIv3JQ https://www.youtube-nocookie.com/embed/ior4nwIv3JQ 2019-12-16 yes https://pycon.de/archive/2019/talks/law-ethics-and-machine-learning-a-curious-menage-a-trois/ 2019-05-26 https://pycon.de/static/media/social/talks/9VGGLP.png Law, ethics and machine learning – a curious ménage à trois https://pycon.de/archive/2019/talks/lessons-learned-as-a-product-manager-in-data-science/ 2019-12-08 https://pycon.de/static/media/social/talks/TS7TZF.png Lessons Learned as a Product Manager in Data Science https://i.ytimg.com/vi/4HDLg2V3nYA/maxresdefault.jpg Lessons Learned as a Product Manager in Data Science The fun part about data science is that no two people really agree on the definition of the data scientist role. So how does the role of product manager in data science look like? https://www.youtube.com/watch?v=4HDLg2V3nYA https://www.youtube-nocookie.com/embed/4HDLg2V3nYA 2019-12-08 yes https://pycon.de/archive/2019/talks/leveraging-the-advantages-of-bayesian-methods-to-build-a-data-science-product-using-pymc3/ 2019-12-08 https://pycon.de/static/media/social/talks/WZWTVB.png Leveraging the advantages of Bayesian Methods to build a data science product using PyMC3 https://i.ytimg.com/vi/gXSAH_nf29M/maxresdefault.jpg Leveraging the advantages of Bayesian Methods to build a data science product using PyMC3 Bayesian models offer greater theoretical advantages compared to non-probabilistic methods, and also allow for more flexible model design. But how can one leverage these theoretical advantages to build a successful data science product? https://www.youtube.com/watch?v=gXSAH_nf29M https://www.youtube-nocookie.com/embed/gXSAH_nf29M 2019-12-08 yes https://pycon.de/archive/2019/talks/loss-function-theory-101/ 2019-12-08 https://pycon.de/static/media/social/talks/JFEQY8.png Loss Function Theory 101 https://i.ytimg.com/vi/0Bw1WGtpMDE/maxresdefault.jpg Loss Function Theory 101 This talk covers the theoretical background behind two common loss functions, mean squared error and cross entropy, including why they are used for machine learning at all, and what limitations you should keep in mind. https://www.youtube.com/watch?v=0Bw1WGtpMDE https://www.youtube-nocookie.com/embed/0Bw1WGtpMDE 2019-12-08 yes https://pycon.de/archive/2019/talks/machine-learning-with-little-data-from-digital-twin-to-predictive-maintenance/ 2019-05-25 https://pycon.de/static/media/social/talks/UMWRYD.png Machine learning with little data - from digital twin to predictive maintenance https://pycon.de/archive/2019/talks/making-the-complex-simple-in-data-viz/ 2019-12-08 https://pycon.de/static/media/social/talks/NC8VVA.png Making the complex simple in data viz https://i.ytimg.com/vi/pwzsGHjTDa4/maxresdefault.jpg Making the complex simple in data viz Creating graphics that convey the desired message, are easily interpretable, but also beautiful can be a daunting task. This talk will demonstrate how to use *The Grammar of Graphics* framework to conceptualize the elements of any graphic, in Python. https://www.youtube.com/watch?v=pwzsGHjTDa4 https://www.youtube-nocookie.com/embed/pwzsGHjTDa4 2019-12-08 yes https://pycon.de/archive/2019/talks/managing-the-end-to-end-machine-learning-lifecycle-with-mlflow/ 2019-12-08 https://pycon.de/static/media/social/talks/PC38WB.png Managing the end-to-end machine learning lifecycle with MLFlow https://i.ytimg.com/vi/6z0_n8kxh-g/maxresdefault.jpg Managing the end-to-end machine learning lifecycle with MLFlow Machine learning requires experimenting with datasets, data preparation steps, and algorithms. Deploy models to a production system and retrain it on new data. MLflow is an open source platform for managing the end-to-end machine learning lifecycle. https://www.youtube.com/watch?v=6z0_n8kxh-g https://www.youtube-nocookie.com/embed/6z0_n8kxh-g 2019-12-08 yes https://pycon.de/archive/2019/talks/mock-hell/ 2019-12-16 https://pycon.de/static/media/social/talks/NZC3A8.png Mock Hell https://i.ytimg.com/vi/CdKaZ7boiZ4/maxresdefault.jpg Mock Hell Mock is an easily abused tool. In perverse cases, it increases technical debt and prevents refactoring. This talk describes the history of mocking, alternatives, anti-patterns, and the connection to clean architecture. https://www.youtube.com/watch?v=CdKaZ7boiZ4 https://www.youtube-nocookie.com/embed/CdKaZ7boiZ4 2019-12-16 yes https://pycon.de/archive/2019/talks/monitoring-infrastructure-and-application-using-django-sensu-and-celery/ 2019-12-16 https://pycon.de/static/media/social/talks/LWALGX.png Monitoring infrastructure and application using Django, Sensu and Celery. https://i.ytimg.com/vi/uo5zV4pyGIE/maxresdefault.jpg Monitoring infrastructure and application using Django, Sensu and Celery. Monitoring is a key aspect for any business. It enables us to find and be notified about our application's problems way ahead our customer notices it, which enables us to keep our businesses running and making customers happy. https://www.youtube.com/watch?v=uo5zV4pyGIE https://www.youtube-nocookie.com/embed/uo5zV4pyGIE 2019-12-16 yes https://pycon.de/archive/2019/talks/optimizing-input-building-your-own-customized-keyboard/ 2019-12-16 https://pycon.de/static/media/social/talks/WJGCNV.png Optimizing Input: Building your own customized keyboard https://i.ytimg.com/vi/j2vrXPdWQck/maxresdefault.jpg Optimizing Input: Building your own customized keyboard Keyboards, the main way we interact with computers, have remained unchanged for a century, despite being free from mechanical necessity. Alternatives made possible by recent technology can optimize the way we input text and interact with our devices. https://www.youtube.com/watch?v=j2vrXPdWQck https://www.youtube-nocookie.com/embed/j2vrXPdWQck 2019-12-16 yes https://pycon.de/archive/2019/talks/package-and-dependency-management-with-poetry/ 2019-12-16 https://pycon.de/static/media/social/talks/CFPRJ8.png Package and Dependency Management with Poetry https://i.ytimg.com/vi/6ey-nNRvBrk/maxresdefault.jpg Package and Dependency Management with Poetry Poetry is “Python packaging and dependency management made easy.”It not only packages up your libraries easily but also isolates environments and resolves dependencies — all with an intuitive command line interface. https://www.youtube.com/watch?v=6ey-nNRvBrk https://www.youtube-nocookie.com/embed/6ey-nNRvBrk 2019-12-16 yes https://pycon.de/archive/2019/talks/panel-turn-any-notebook-into-a-deployable-dashboard/ 2019-12-08 https://pycon.de/static/media/social/talks/ZT9NY9.png Panel: Turn any notebook into a deployable dashboard https://i.ytimg.com/vi/Ohr29FJjBi0/maxresdefault.jpg Panel: Turn any notebook into a deployable dashboard Quickly turn your existing analyses built on the PyData stack into shareable, standalone apps and dashboards using the new open-source [Panel](http://panel.pyviz.org) library without learning the details of web development. https://www.youtube.com/watch?v=Ohr29FJjBi0 https://www.youtube-nocookie.com/embed/Ohr29FJjBi0 2019-12-08 yes https://pycon.de/archive/2019/talks/parallel-programming-for-python-developers-lets-go-lang/ 2019-12-16 https://pycon.de/static/media/social/talks/9AKC3R.png Parallel programming for python developers – Let’s Go(lang) https://i.ytimg.com/vi/MZjjPn7IpgQ/maxresdefault.jpg Parallel programming for python developers – Let’s Go(lang) Despite all of python's strengths, parallel programming tends to not be one of them. Enter Go, a language that has parallel programming at its core. In this tutorial we will build an API pipeline service in Go, that calls several APIs in parallel. https://www.youtube.com/watch?v=MZjjPn7IpgQ https://www.youtube-nocookie.com/embed/MZjjPn7IpgQ 2019-12-16 yes https://pycon.de/archive/2019/talks/pep-581-and-pep-588-migrating-cpython-s-issue-tracker/ 2019-12-16 https://pycon.de/static/media/social/talks/DFCU3K.png PEP 581 and PEP 588: Migrating CPython's Issue Tracker https://i.ytimg.com/vi/tBY8u7iSF0M/maxresdefault.jpg PEP 581 and PEP 588: Migrating CPython's Issue Tracker The acceptance of PEP 581, by Python steering council means that another big workflow change is impending. Let's hear about some of the proposed plans on improving CPython's workflow, and learn how you can help and take part in this process. https://www.youtube.com/watch?v=tBY8u7iSF0M https://www.youtube-nocookie.com/embed/tBY8u7iSF0M 2019-12-16 yes https://pycon.de/archive/2019/talks/play-stupid-games-win-stupid-prizes/ 2019-12-08 https://pycon.de/static/media/social/talks/PY8XW9.png Play Stupid Games, Win Stupid Prizes https://i.ytimg.com/vi/zZXSGzlVxvU/maxresdefault.jpg Play Stupid Games, Win Stupid Prizes This is reserved for a James Powell in-promptu talk, stay tuned! This is reserved for a James Powell in-promptu talk, stay tuned! https://www.youtube.com/watch?v=zZXSGzlVxvU https://www.youtube-nocookie.com/embed/zZXSGzlVxvU 2019-12-08 yes https://pycon.de/archive/2019/talks/practical-devops-for-the-busy-data-scientist/ 2019-12-08 https://pycon.de/static/media/social/talks/AEAT3E.png Practical DevOps for the busy data scientist https://i.ytimg.com/vi/PuDvyea2x8I/maxresdefault.jpg Practical DevOps for the busy data scientist How many times have you developed a model or a data application and tested it locally or in a staging environment just to find out that it breaks in production? This is a common issue faced by thousands of data scientists around the globe. As the wor https://www.youtube.com/watch?v=PuDvyea2x8I https://www.youtube-nocookie.com/embed/PuDvyea2x8I 2019-12-08 yes https://pycon.de/archive/2019/talks/privacy-preserving-machine-learning-for-text-processing/ 2019-12-08 https://pycon.de/static/media/social/talks/MF9DAV.png Privacy-preserving Machine Learning for text processing https://i.ytimg.com/vi/M_wnxin97U0/maxresdefault.jpg Privacy-preserving Machine Learning for text processing Privacy is something we all care about, but when it is time to put our principles into application, it is not so trivial, especially when working with text. This talk aims at presenting a few options to handle privacy when dealing with text. https://www.youtube.com/watch?v=M_wnxin97U0 https://www.youtube-nocookie.com/embed/M_wnxin97U0 2019-12-08 yes https://pycon.de/archive/2019/talks/production-level-data-pipelines-that-make-everyone-happy-using-kedro/ 2019-05-20 https://pycon.de/static/media/social/talks/FXUYQ9.png Production-level data pipelines that make everyone happy using Kedro https://pycon.de/archive/2019/talks/professional-development-and-career-progression-for-data-scientists/ 2019-12-08 https://pycon.de/static/media/social/talks/E3LFDL.png Professional Development and Career Progression for Data Scientists https://i.ytimg.com/vi/Bt7-7yXG8A4/maxresdefault.jpg Professional Development and Career Progression for Data Scientists In this talk you will learn how to level up your skills, and develop your your career using on the job opportunities, as well as open source contributions https://www.youtube.com/watch?v=Bt7-7yXG8A4 https://www.youtube-nocookie.com/embed/Bt7-7yXG8A4 2019-12-08 yes https://pycon.de/archive/2019/talks/pytest-simple-rapid-and-fun-testing-with-python/ 2019-12-16 https://pycon.de/static/media/social/talks/WRHHSF.png pytest - simple, rapid and fun testing with Python https://i.ytimg.com/vi/CMuSn9cofbI/maxresdefault.jpg pytest - simple, rapid and fun testing with Python The pytest tool presents a rapid and simple way to write tests for your Python code. This training gives an introduction with exercises to some distinguishing features. https://www.youtube.com/watch?v=CMuSn9cofbI https://www.youtube-nocookie.com/embed/CMuSn9cofbI 2019-12-16 yes https://pycon.de/archive/2019/talks/python-2020/ 2019-12-16 https://pycon.de/static/media/social/talks/JFNCMQ.png Python 2020+ https://i.ytimg.com/vi/fOdCxum-qLA/maxresdefault.jpg Python 2020+ While very successful, Python's peculiarly missing in some spaces like mobile devices, client-side Web, or gaming. Should we do something about it? How could we go about changing that? https://www.youtube.com/watch?v=fOdCxum-qLA https://www.youtube-nocookie.com/embed/fOdCxum-qLA 2019-12-16 yes https://pycon.de/archive/2019/talks/python-panel/ 2019-10-05 https://pycon.de/static/media/social/talks/7JH7XX.png Python Panel https://pycon.de/archive/2019/talks/python-powered-osint-modernising-open-source-intelligence-for-investigating-disinformation/ 2019-05-25 https://pycon.de/static/media/social/talks/9LZBX9.png Python-Powered OSINT! Modernising Open Source Intelligence for Investigating Disinformation https://pycon.de/archive/2019/talks/quantum-computing-with-python/ 2019-12-08 https://pycon.de/static/media/social/talks/RE99DB.png Quantum computing with Python https://i.ytimg.com/vi/tjx2b-cJktE/maxresdefault.jpg Quantum computing with Python Frameworks for quantum computing are a new way to use Python for cutting-edge science, and to plan for future applications of this new technology. This session will serve as an introduction to quantum computing as a whole, and also to Qiskit, the most well-developed and well-used quantum Python framework. And to make sure I don't get too complex, we'll do it all on a microcontroller! https://www.youtube.com/watch?v=tjx2b-cJktE https://www.youtube-nocookie.com/embed/tjx2b-cJktE 2019-12-08 yes https://pycon.de/archive/2019/talks/refactoring-in-python-design-patterns-and-approaches/ 2019-12-16 https://pycon.de/static/media/social/talks/FTVNPK.png Refactoring in Python: Design Patterns and Approaches https://i.ytimg.com/vi/ZzKaFJxiDzA/maxresdefault.jpg Refactoring in Python: Design Patterns and Approaches Experiences and lessons learned from tackling extremely demanding code. How to bring order to mismanaged code and elevate the code base to a standard that's acceptable in today's tech environment. https://www.youtube.com/watch?v=ZzKaFJxiDzA https://www.youtube-nocookie.com/embed/ZzKaFJxiDzA 2019-12-16 yes https://pycon.de/archive/2019/talks/rethinking-open-source-in-the-era-of-cloud-machine-learning/ 2019-12-08 https://pycon.de/static/media/social/talks/GGR9ZM.png Rethinking Open Source in the Era of Cloud & Machine Learning https://i.ytimg.com/vi/QMJIh-voWng/maxresdefault.jpg Rethinking Open Source in the Era of Cloud & Machine Learning By some measures, Open Source is a wildly successful and crucial part of many areas of modern technology. However, the ’sustainability crisis’ and the age of cloud computing have threatened its core mechanisms. Peter will present some alternative ways of looking at this crucial moment in the evolution of the open source movement, and suggest some ways to think about the future of open source. https://www.youtube.com/watch?v=QMJIh-voWng https://www.youtube-nocookie.com/embed/QMJIh-voWng 2019-12-08 yes https://pycon.de/archive/2019/talks/running-an-open-source-project-like-a-start-up/ 2019-12-16 https://pycon.de/static/media/social/talks/9ARCJA.png Running An Open Source Project Like A Start Up https://i.ytimg.com/vi/ZhE-xdJ6V34/maxresdefault.jpg Running An Open Source Project Like A Start Up Not so long ago, I started an open source project - PicknMIx. It feels like running a start up if you are serious about it. Want to know my story? Want to check if you can do it as well? I will tell you. (Stickers not guaranteed though) https://www.youtube.com/watch?v=ZhE-xdJ6V34 https://www.youtube-nocookie.com/embed/ZhE-xdJ6V34 2019-12-16 yes https://pycon.de/archive/2019/talks/should-i-stay-or-should-i-go-optimal-exercise-decisions-using-the-longstaff-schwartz-algorithm/ 2019-05-25 https://pycon.de/static/media/social/talks/MAPYAH.png Should I stay or should I go? Optimal exercise decisions using the Longstaff-Schwartz algorithm https://pycon.de/archive/2019/talks/skorch-a-scikit-learn-compatible-neural-network-library-that-wraps-pytorch/ 2019-05-16 https://pycon.de/static/media/social/talks/NMXSE7.png skorch: A scikit-learn compatible neural network library that wraps pytorch https://pycon.de/archive/2019/talks/static-typing-in-python/ 2019-12-16 https://pycon.de/static/media/social/talks/NH9UEY.png Static Typing in Python https://i.ytimg.com/vi/p-nhGq-Wwv8/maxresdefault.jpg Static Typing in Python In this talk, we'll discuss the advantages and disadvantages to a static type system, as well as recent efforts to introduce static typing to Python via optional "type hints" and various tools to aid in adding types to Python code. https://www.youtube.com/watch?v=p-nhGq-Wwv8 https://www.youtube-nocookie.com/embed/p-nhGq-Wwv8 2019-12-16 yes https://pycon.de/archive/2019/talks/strawberry-a-dataclasses-inspired-approach-to-graphql/ 2019-12-16 https://pycon.de/static/media/social/talks/QABWWM.png Strawberry: a dataclasses inspired approach to GraphQL https://i.ytimg.com/vi/J9AYhCmKMzo/maxresdefault.jpg Strawberry: a dataclasses inspired approach to GraphQL Over the past few years, GraphQL has gained much traction, especially in the JavaScript world. Python is getting on board this trend with new interesting libraries. In this talk, we will see how Strawberry makes uses of dataclasses and type hints to https://www.youtube.com/watch?v=J9AYhCmKMzo https://www.youtube-nocookie.com/embed/J9AYhCmKMzo 2019-12-16 yes https://pycon.de/archive/2019/talks/tackle-the-problems-that-really-matter-leverage-the-power-of-data-science-in-the-service-of-humanity/ 2019-05-26 https://pycon.de/static/media/social/talks/PZVHP9.png Tackle the problems that really matter - leverage the power of data science in the service of humanity https://pycon.de/archive/2019/talks/take-control-of-your-hearing-accessible-methods-to-build-a-smart-noise-filter/ 2019-06-08 https://pycon.de/static/media/social/talks/JZW9HE.png Take control of your hearing: Accessible methods to build a smart noise filter https://pycon.de/archive/2019/talks/the-sound-of-silence-online-misogyny-and-how-we-model-it/ 2019-12-08 https://pycon.de/static/media/social/talks/JUQMXE.png The Sound of Silence: Online Misogyny and How we Model it https://i.ytimg.com/vi/kd63M0HDDrg/maxresdefault.jpg The Sound of Silence: Online Misogyny and How we Model it Female-identifying people are being attacked and silenced online. Social media platforms act as neutral bodies and law enforcement can’t stop the abuse. When you can’t trust that your safety will be protected online, what can you do? We say, Opt Out. https://www.youtube.com/watch?v=kd63M0HDDrg https://www.youtube-nocookie.com/embed/kd63M0HDDrg 2019-12-08 yes https://pycon.de/archive/2019/talks/time-series-anomaly-detection-for-bottling-machine-maintenance/ 2019-05-25 https://pycon.de/static/media/social/talks/7XCL3Z.png Time Series Anomaly Detection for Bottling Machine Maintenance https://pycon.de/archive/2019/talks/time-series-modelling-with-probabilistic-programming/ 2019-05-14 https://pycon.de/static/media/social/talks/AX9CSK.png Time series modelling with probabilistic programming https://pycon.de/archive/2019/talks/tools-that-help-you-get-your-experiments-under-control/ 2019-12-08 https://pycon.de/static/media/social/talks/CWMAE7.png Tools that help you get your experiments under control https://i.ytimg.com/vi/Rb3_FTu7Ftc/maxresdefault.jpg Tools that help you get your experiments under control There is now a wealth of tools that support data science best practices (e.g. tracking experiments, versioning data). Let’s take a look at which tools are available and which ones might be right for your project. https://www.youtube.com/watch?v=Rb3_FTu7Ftc https://www.youtube-nocookie.com/embed/Rb3_FTu7Ftc 2019-12-08 yes https://pycon.de/archive/2019/talks/transforming-a-legacy-system-into-a-bias-mitigating-ai-solution-for-debt-repayment/ 2019-05-18 https://pycon.de/static/media/social/talks/PMDFA3.png Transforming a Legacy System into a Bias-Mitigating AI Solution for Debt Repayment https://pycon.de/archive/2019/talks/using-adversarial-samples-to-break-and-robustify-your-vision-neural-network-models/ 2019-05-22 https://pycon.de/static/media/social/talks/GKHSBP.png Using adversarial samples to break and robustify your Vision Neural Network Models https://pycon.de/archive/2019/talks/using-machine-learning-for-level-generation-in-snake-video-game/ 2019-12-08 https://pycon.de/static/media/social/talks/VCSBUY.png Using machine learning for Level Generation in Snake (video-game) https://i.ytimg.com/vi/pBhHvXyFi7Y/maxresdefault.jpg Using machine learning for Level Generation in Snake (video-game) As a practical example, this tutorial uses machine learning models to predict where to best place the apple in Snake. By using datasets that contain different plays we can obtain different game experiences or models that can adapt to the style of a player. https://www.youtube.com/watch?v=pBhHvXyFi7Y https://www.youtube-nocookie.com/embed/pBhHvXyFi7Y 2019-12-08 yes https://pycon.de/archive/2019/talks/using-micropython-to-develop-an-iot-multimode-sensor-platform-with-an-augmented-reality-ui/ 2019-12-16 https://pycon.de/static/media/social/talks/7X799R.png Using Micropython to develop an IoT multimode sensor platform with an Augmented Reality UI https://i.ytimg.com/vi/hhWX6DD9QhU/maxresdefault.jpg Using Micropython to develop an IoT multimode sensor platform with an Augmented Reality UI Building a sensor platform that is flexible and intuitive is hard! This talk takes you through a journey how a sensor platform was developed to create a sensor farm for the purpose of capturing and acquiring data to be used in AI systems for Samsung. https://www.youtube.com/watch?v=hhWX6DD9QhU https://www.youtube-nocookie.com/embed/hhWX6DD9QhU 2019-12-16 yes https://pycon.de/archive/2019/talks/using-overhead-video-capture-to-analyse-grouping-behaviour-of-dancers-in-a-silent-disco/ 2019-05-26 https://pycon.de/static/media/social/talks/RRPC79.png Using Overhead Video Capture to Analyse Grouping Behaviour of Dancers in a Silent Disco https://pycon.de/archive/2019/talks/version-control-for-data-science/ 2019-12-08 https://pycon.de/static/media/social/talks/PPGWXL.png Version Control for Data Science https://i.ytimg.com/vi/IH2gEtxIbqM/maxresdefault.jpg Version Control for Data Science Are you versioning your Machine Learning project as you would do in a traditional software project? How are you keeping track of changes in your datasets? https://www.youtube.com/watch?v=IH2gEtxIbqM https://www.youtube-nocookie.com/embed/IH2gEtxIbqM 2019-12-08 yes https://pycon.de/archive/2019/talks/visualizing-interactive-graph-networks-in-python/ 2019-12-08 https://pycon.de/static/media/social/talks/HFKNUW.png Visualizing Interactive Graph Networks in Python https://i.ytimg.com/vi/h1OoqnzAh2E/maxresdefault.jpg Visualizing Interactive Graph Networks in Python In this talk you will learn how to visualize graph networks in Python, using `networkx`, `traitlets`, `ipywidgets` and `plotly`. The resulting plot will be fully interactive, which makes it easy to filter edges, find nodes by their name and update the graph's layout. https://www.youtube.com/watch?v=h1OoqnzAh2E https://www.youtube-nocookie.com/embed/h1OoqnzAh2E 2019-12-08 yes https://pycon.de/archive/2019/talks/vtext-text-processing-in-rust-with-python-bindings/ 2019-12-08 https://pycon.de/static/media/social/talks/ALRGXS.png vtext: text processing in Rust with Python bindings https://i.ytimg.com/vi/G_43eu6Ascs/maxresdefault.jpg vtext: text processing in Rust with Python bindings In this we talk present how to write Python extensions in Rust, and discusse advantages and limitation of such approach. We then illustrate this approach on the vtext project, that aims to be a high-performance library for text processing. https://www.youtube.com/watch?v=G_43eu6Ascs https://www.youtube-nocookie.com/embed/G_43eu6Ascs 2019-12-08 yes https://pycon.de/archive/2019/talks/want-to-have-a-positive-social-impact-as-a-data-scientist/ 2019-12-08 https://pycon.de/static/media/social/talks/JSJLG9.png Want to have a positive social impact as a data scientist? https://i.ytimg.com/vi/LMh3i1EXhp8/maxresdefault.jpg Want to have a positive social impact as a data scientist? Discover your individual approach towards more positive social impact by conducting experiments. I'll show you how! I’ll also share my learnings from doing such experiments over the last 15 years. https://www.youtube.com/watch?v=LMh3i1EXhp8 https://www.youtube-nocookie.com/embed/LMh3i1EXhp8 2019-12-08 yes https://pycon.de/archive/2019/talks/what-if-i-tell-you-that-your-specs-are-broken/ 2019-12-16 https://pycon.de/static/media/social/talks/NFH83Z.png What if I tell you that your specs are broken https://i.ytimg.com/vi/wd0-bPZD52Y/maxresdefault.jpg What if I tell you that your specs are broken This talk is going to be about Swagger Specs and their changes. We're going to examine what Backward Incompatible changes are and how you can deal with them. The talk will also introduce you to a tool that will help you on having safer changes. https://www.youtube.com/watch?v=wd0-bPZD52Y https://www.youtube-nocookie.com/embed/wd0-bPZD52Y 2019-12-16 yes https://pycon.de/archive/2019/talks/what-we-learned-from-scraping-1-billion-webpages-every-month/ 2019-12-16 https://pycon.de/static/media/social/talks/Q88ZUW.png What we learned from scraping 1 billion webpages every month https://i.ytimg.com/vi/w85WZ0mJQS4/maxresdefault.jpg What we learned from scraping 1 billion webpages every month Web is broken. We learned the hard way. Developers tend to hack, hacks tend to break the web. In this talk, I share what we learned how websites don't obey the protocols and how developers had caused the web became a chaotic medium. https://www.youtube.com/watch?v=w85WZ0mJQS4 https://www.youtube-nocookie.com/embed/w85WZ0mJQS4 2019-12-16 yes https://pycon.de/archive/2019/talks/whats-new-in-python-3-8/ 2019-12-16 https://pycon.de/static/media/social/talks/CATV3T.png What’s new in Python 3.8? https://i.ytimg.com/vi/vTxXHx5lfv0/maxresdefault.jpg What’s new in Python 3.8? In few months, there will be the release of Python 3.8! As a core dev I would like to show you the new features of this version. https://www.youtube.com/watch?v=vTxXHx5lfv0 https://www.youtube-nocookie.com/embed/vTxXHx5lfv0 2019-12-16 yes https://pycon.de/archive/2019/talks/where-linguistics-meets-natural-language-processing/ 2019-12-08 https://pycon.de/static/media/social/talks/SGPLXB.png Where Linguistics meets Natural Language Processing https://i.ytimg.com/vi/vesSXYx2uQw/maxresdefault.jpg Where Linguistics meets Natural Language Processing This talk explains how linguistics describes language - via phonetics-phonology, morphology, syntax, semantics and pragmatics. We will combine linguistic concepts with models through examples for NLP newbies. https://www.youtube.com/watch?v=vesSXYx2uQw https://www.youtube-nocookie.com/embed/vesSXYx2uQw 2019-12-08 yes https://pycon.de/archive/2019/talks/why-you-dont-see-many-real-world-applications-of-reinforcement-learning/ 2019-05-12 https://pycon.de/static/media/social/talks/GTHCWX.png Why you don’t see many real-world applications of Reinforcement Learning. https://pycon.de/archive/2019/talks/why-you-should-not-train-your-own-bert-model-for-different-languages-or-domains/ 2019-05-25 https://pycon.de/static/media/social/talks/YAJRGX.png Why you should (not) train your own BERT model for different languages or domains https://pycon.de/archive/2019/talks/write-your-own-decorators/ 2019-12-16 https://pycon.de/static/media/social/talks/ASLHQN.png Write your Own Decorators https://i.ytimg.com/vi/lWo0Zjgj3ZA/maxresdefault.jpg Write your Own Decorators Decorators are really useful. Using them is simple. Writing your own is bit more involved. Learn in this hands-on workshop how to write decorators for many different purposes. The emphasis is on best practices and practical examples. https://www.youtube.com/watch?v=lWo0Zjgj3ZA https://www.youtube-nocookie.com/embed/lWo0Zjgj3ZA 2019-12-16 yes https://pycon.de/archive/2019/talks/your-name-is-invalid/ 2019-12-08 https://pycon.de/static/media/social/talks/ZNA8WN.png Your Name Is Invalid! https://i.ytimg.com/vi/pBuS7EUPnQA/maxresdefault.jpg Your Name Is Invalid! About people with first names, middle names, last names, one-word names, multiple names, and changing names, about names with characters beyond ASCII, and about using Python to handle them correctly, because names of people cannot be invalid. https://www.youtube.com/watch?v=pBuS7EUPnQA https://www.youtube-nocookie.com/embed/pBuS7EUPnQA 2019-12-08 yes https://pycon.de/archive/2022/talks/5-steps-to-speed-up-your-data-analysis-on-a-single-core/ 2022-05-06 https://pycon.de/static/media/social/talks/VYS8XY.png 5 Steps to Speed Up Your Data-Analysis on a Single Core https://i.ytimg.com/vi/7Tka8UureD0/maxresdefault.jpg 5 Steps to Speed Up Your Data-Analysis on a Single Core Your data analysis pipeline works. *Nice.* Could it be faster? *Probably.* Do you need to parallelize? *Not yet.* We'll go through optimization steps that **boost the performance of your data analysis pipeline on a single core**, reducing time & costs. This walkthrough shows tools and strategies to identify and mitigate bottlenecks, and demonstrate them in an example. The 5 steps cover profiling, memory optimizations, and various speedups such as jit-ing with numba. https://www.youtube.com/watch?v=7Tka8UureD0 https://www.youtube-nocookie.com/embed/7Tka8UureD0 2022-05-06 yes https://pycon.de/archive/2022/talks/5-things-we-ve-learned-building-large-apis-with-fastapi/ 2022-05-06 https://pycon.de/static/media/social/talks/KXJP7B.png 5 Things we've learned building large APIs with FastAPI https://i.ytimg.com/vi/DpCuo5fiKME/maxresdefault.jpg 5 Things we've learned building large APIs with FastAPI FastAPI has become a very popular framework for building high-performance and well documented APIs in Python 3.6+. In this talk we share 5 things we've learned while building multiple APIs at InvestSuite. https://www.youtube.com/watch?v=DpCuo5fiKME https://www.youtube-nocookie.com/embed/DpCuo5fiKME 2022-05-06 yes https://pycon.de/archive/2022/talks/5-things-you-want-to-know-about-ai-adoption-in-the-enterprise/ 2022-05-06 https://pycon.de/static/media/social/talks/EMNPJW.png 5 Things You Want to Know About AI Adoption in the Enterprise https://i.ytimg.com/vi/0UG_JLUWJOQ/maxresdefault.jpg 5 Things You Want to Know About AI Adoption in the Enterprise All one needs is strategy, skill and resources to make digitalization and AI happen. So why is everything taking so long? Shouldn’t you all be finished yesterday already? Or: how do we start? A practitioner's talk for everyone involved making AI happen in enterprises with use cases. https://www.youtube.com/watch?v=0UG_JLUWJOQ https://www.youtube-nocookie.com/embed/0UG_JLUWJOQ 2022-05-06 yes https://pycon.de/archive/2022/talks/5-years-10-sprints-a-scikit-learn-open-source-journey/ 2022-05-06 https://pycon.de/static/media/social/talks/CLGY3M.png 5 Years, 10 Sprints, A scikit-learn Open Source Journey https://i.ytimg.com/vi/ZUqJaCWPvmk/maxresdefault.jpg 5 Years, 10 Sprints, A scikit-learn Open Source Journey We all use open source tools in various capacities, yet knowing how to contribute to open source is not as well known or accessible. The limited knowledge and education surrounding contributing to open source could be one explanation of the low participation rates by underrepresented persons in open source. Open source sprints are hands-on “workshops” or “hackathons” where contributors collaborate to resolve coding and documentation issues posted on a GitHub repository. I will share how I organized my first open source sprint in 2017, which was in-person and held in New York City. Over the next 5 years, I organized in-person sprints from San Francisco, USA to Nairobi, Kenya, as well as pivoting to online sprints due to the global pandemic. In this keynote, I will share highlights, challenges and lessons learned. (https://www.dataumbrella.org/sprints). https://www.youtube.com/watch?v=ZUqJaCWPvmk https://www.youtube-nocookie.com/embed/ZUqJaCWPvmk 2022-05-06 yes https://pycon.de/archive/2022/talks/a-data-scientist-s-guide-to-code-reviews/ 2022-05-06 https://pycon.de/static/media/social/talks/YT7WM7.png A data scientist's guide to code reviews https://i.ytimg.com/vi/h8oI24i9dPk/maxresdefault.jpg A data scientist's guide to code reviews A crucial aspect of software engineering teams' working agreements are code reviews. By applying the four-eyes principle on code, teams can reduce the number of bugs and errors, uncover misunderstandings early and ensure a certain level of quality across their common code base. In essence, the relevance of code reviews does not change for data teams, including data scientists. However, due to the often experimental nature of data science tasks, standard code reviews do not always work well and therefore need some tweaks. This talk will give a data scientist's view on code reviews, focussing on which aspects data scientists can pull from the general process and what needs to be adjusted in order to have effective and satisfying code reviews. Building on that, you will get recommendations for the following questions: * When and what should I review? * What feedback should I give? * What tools support me in executing this task? https://www.youtube.com/watch?v=h8oI24i9dPk https://www.youtube-nocookie.com/embed/h8oI24i9dPk 2022-05-06 yes https://pycon.de/archive/2022/talks/a-smooth-ride-online-car-buying-and-selling-at-mobile-de/ 2022-03-14 https://pycon.de/static/media/social/talks/TDLMHQ.png A Smooth Ride: Online Car Buying and Selling at mobile.de https://pycon.de/archive/2022/talks/advanced-django-orm/ 2022-05-06 https://pycon.de/static/media/social/talks/W8KUG3.png Advanced Django ORM https://i.ytimg.com/vi/F1vaEBDxAig/maxresdefault.jpg Advanced Django ORM The Django ORM is one of the most powerful ORM in the Python space. We will look at implementing complex queries and constraints as well as performance optimisations for queries and models in Django ORM. https://www.youtube.com/watch?v=F1vaEBDxAig https://www.youtube-nocookie.com/embed/F1vaEBDxAig 2022-05-06 yes https://pycon.de/archive/2022/talks/an-introduction-to-inter-process-communication-and-synchronization-using-python/ 2022-01-16 https://pycon.de/static/media/social/talks/VCTPVE.png An Introduction to Inter Process Communication and Synchronization using Python https://pycon.de/archive/2022/talks/aspect-oriented-programming-diving-deep-into-decorators/ 2022-05-06 https://pycon.de/static/media/social/talks/L8XNVK.png Aspect-oriented Programming - Diving deep into Decorators https://i.ytimg.com/vi/FMsap3_d_To/maxresdefault.jpg Aspect-oriented Programming - Diving deep into Decorators The aspect-oriented programming paradigm can support the separation of cross-cutting concerns such as logging, caching, or checking of permissions. This can improve code modularity and maintainability. Python offers decorator to implement re-usable code for cross-cutting task. This tutorial is an in-depth introduction to decorators. It covers the usage of decorators and how to implement simple and more advanced decorators. Use cases demonstrate how to work with decorators. In addition to showing how functions can use closures to create decorators, the tutorial introduces callable class instance as alternative. Class decorators can solve problems that use be to be tasks for metaclasses. The tutorial provides uses cases for class decorators. While the focus is on best practices and practical applications, the tutorial also provides deeper insight into how Python works behind the scene. After the tutorial participants will feel comfortable with functions that take functions and return new functions. https://www.youtube.com/watch?v=FMsap3_d_To https://www.youtube-nocookie.com/embed/FMsap3_d_To 2022-05-06 yes https://pycon.de/archive/2022/talks/battle-of-pipelines-who-will-win-python-orchestration-in-2022/ 2022-05-06 https://pycon.de/static/media/social/talks/DPFV7C.png Battle of Pipelines - who will win python orchestration in 2022? https://i.ytimg.com/vi/xy5hLKfGGTs/maxresdefault.jpg Battle of Pipelines - who will win python orchestration in 2022? Python is used for a wide variety of applications in data science, scientific computing, and other complex enterprise-level products. When building production-ready products, there is no way around using a suitable framework that triggers the different set of pipelines handling the dataflow. Nowadays, there is an enormous landscape of orchestration tools, making it easy to lose track of their various advantages and disadvantages. This talk will take a look at the current landscape of orchestration tools in Python. Specifically focusing on Kubeflow, Airflow and Prefect and which of them are best suited in terms of performance, usability, and effort of implementation and maintenance. https://www.youtube.com/watch?v=xy5hLKfGGTs https://www.youtube-nocookie.com/embed/xy5hLKfGGTs 2022-05-06 yes https://pycon.de/archive/2022/talks/beyond-the-basics-contributor-experience-diversity-and-culture-in-open-source-projects/ 2022-02-08 https://pycon.de/static/media/social/talks/NVBLKH.png Beyond the basics: Contributor experience, diversity and culture in open source projects https://pycon.de/archive/2022/talks/biases-in-language-models/ 2022-05-06 https://pycon.de/static/media/social/talks/HXCMKR.png Biases in Language Models https://i.ytimg.com/vi/SdjmhbFNR_Q/maxresdefault.jpg Biases in Language Models The talk is an attempt to measure biases in most popular language models and we propose a solution to reduce the bias, and promote social inclusion and diversity based on gender. We have covered both methods on contextual and non contextual word em- bedding debiasing techniques. We have also tried to compare the biases in different models, like Flair, Bert and glove. The dataset used is Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). The use of AI in sensitive areas including for hiring, criminal justice and health- care makes it more important to look under the hood for bias and fairness. AI being shaped by flawed and societal biases. Underlying data rather than the algorithm itself are most often the main source of the issue. and how can we use finetuning and projection methods to overcome those biases in models There have been several cases where google translator or any other language models have given racial or gender biased results. When a gender neutral language like finnish is translated to English it gives male biased results. Due to word embeddings trained on news articles may exhibit the gender stereotypes found in society. We have finetuned model and have tried debiasing non contextual embeddings. https://www.youtube.com/watch?v=SdjmhbFNR_Q https://www.youtube-nocookie.com/embed/SdjmhbFNR_Q 2022-05-06 yes https://pycon.de/archive/2022/talks/building-a-sign-to-speech-prototype-with-tensorflow-pytorch-and-deepstack-how-it-happened-what-i-learned/ 2022-01-08 https://pycon.de/static/media/social/talks/WWPUGX.png Building a Sign-to-Speech prototype with TensorFlow, Pytorch and DeepStack: How it happened & What I learned https://pycon.de/archive/2022/talks/building-an-orm-from-scratch/ 2022-05-06 https://pycon.de/static/media/social/talks/LKHQ9J.png Building an ORM from scratch https://i.ytimg.com/vi/1NN__kmMtAQ/maxresdefault.jpg Building an ORM from scratch Have you ever wondered how Django, SQLAlchemy, and PonyORM can translate Python objects into database rows? How complicated can this really be? In this talk we will be creating our own (limited) ORM from scratch, starting from almost nothing and finishing with a library that can define relational models, translate simple and (somewhat) complex queries into a database query, and create and apply migrations, all while hiding the details behind a Python interface. https://www.youtube.com/watch?v=1NN__kmMtAQ https://www.youtube-nocookie.com/embed/1NN__kmMtAQ 2022-05-06 yes https://pycon.de/archive/2022/talks/but-this-is-an-oauth-is-it-not/ 2022-05-06 https://pycon.de/static/media/social/talks/XKJYW3.png But this is an OAuth, is it not? https://i.ytimg.com/vi/VIS4ArPJ_Dw/maxresdefault.jpg But this is an OAuth, is it not? OAuth simplified and secured third-party integrations for the end user. But for the developer of the integration, it can still present some friction. This talk talks about examples of real-life problems that were encountered by implementing multiple OAuth integrations. https://www.youtube.com/watch?v=VIS4ArPJ_Dw https://www.youtube-nocookie.com/embed/VIS4ArPJ_Dw 2022-05-06 yes https://pycon.de/archive/2022/talks/can-you-read-this-or-how-i-improved-text-readability-on-the-web-for-the-visually-impaired/ 2022-01-16 https://pycon.de/static/media/social/talks/MCLBLZ.png Can you Read This? (Or: how I Improved Text Readability on the Web for the Visually Impaired) https://pycon.de/archive/2022/talks/career-panel/ 2022-05-06 https://pycon.de/static/media/social/talks/SDQEB8.png Career Panel https://i.ytimg.com/vi/ae1IOcdonNE/maxresdefault.jpg Career Panel Working in the 21st century is very different from the past. Especially the digital space offers many opportunities to work in the office and remotely. In your hometown or somewhere else on the globe. Some companies even work completely remotely. Or, as a freelancer, you can be your own boss. Or running a company, where you are in charge of all the decisions. This panel invites folks with diverse careers to ponder the differences in these choices and give advice to you (and their former selves!) on choices along the way. https://www.youtube.com/watch?v=ae1IOcdonNE https://www.youtube-nocookie.com/embed/ae1IOcdonNE 2022-05-06 yes https://pycon.de/archive/2022/talks/challenge-accepted-how-to-escape-the-quicksand-while-engineering-a-computer-vision-application/ 2022-01-15 https://pycon.de/static/media/social/talks/BHZG8Z.png Challenge Accepted - How to Escape the Quicksand While Engineering a Computer Vision Application https://pycon.de/archive/2022/talks/come-as-you-are-transitioning-from-science-to-data-science/ 2022-05-06 https://pycon.de/static/media/social/talks/3L8NHL.png Come as you are: Transitioning from Science to Data Science https://i.ytimg.com/vi/rUyPurQEszc/maxresdefault.jpg Come as you are: Transitioning from Science to Data Science I would like to give a little insight into my journey from academia to industry. I started working as a data scientist after more than a decade in quantitative science and leaving my post-doc position in neuroscience. Now I am often asked by scientists how I made the transition. In my talk, I want to encourage newcomers, show them what they might already bring to the table, help them avoid pitfalls, and explain what I think might be helpful in finding a first job as a data scientist. I'll also touch on the tech stack needed and what role Python plays in that. Come as you are! https://www.youtube.com/watch?v=rUyPurQEszc https://www.youtube-nocookie.com/embed/rUyPurQEszc 2022-05-06 yes https://pycon.de/archive/2022/talks/conda-forge-supporting-the-growth-of-the-volunteer-driven-community-based-packaging-project/ 2021-12-24 https://pycon.de/static/media/social/talks/TPZHC7.png conda-forge: supporting the growth of the volunteer-driven, community-based packaging project https://pycon.de/archive/2022/talks/creating-3d-maps-using-python/ 2022-05-06 https://pycon.de/static/media/social/talks/V3CCHQ.png Creating 3D Maps using Python https://i.ytimg.com/vi/ie-WzjxWJ94/maxresdefault.jpg Creating 3D Maps using Python In this talk it is shown how to create 3D Maps using Open Data and Python. There are many open data sources available now for direct download, for example on AWS ( https://aws.amazon.com/opendata ). This talks shows how to download and process the data to create textured 3D models. https://www.youtube.com/watch?v=ie-WzjxWJ94 https://www.youtube-nocookie.com/embed/ie-WzjxWJ94 2022-05-06 yes https://pycon.de/archive/2022/talks/data-apis-standardization-of-n-dimensional-arrays-and-dataframes/ 2022-05-06 https://pycon.de/static/media/social/talks/BMFVFG.png Data Apis: Standardization of N-dimensional arrays and dataframes https://i.ytimg.com/vi/__EkpdeVGY4/maxresdefault.jpg Data Apis: Standardization of N-dimensional arrays and dataframes We would like to introduce the consortium of Data APIs, where we will be presenting our motivation, objectives and progress of the standardization process after one year of activity. We will dive into a small history lesson of the current state of the Python data and scientific ecosystem and understand the current fragmentation of the APIs. Then, we will start discussing the efforts of standardization for N-dimensional arrays and dataframes. Finally. we will talk about the roadmap for the next year. https://www.youtube.com/watch?v=__EkpdeVGY4 https://www.youtube-nocookie.com/embed/__EkpdeVGY4 2022-05-06 yes https://pycon.de/archive/2022/talks/data-science-at-scale-with-dask/ 2022-05-06 https://pycon.de/static/media/social/talks/RTPEWV.png Data Science at Scale with Dask https://i.ytimg.com/vi/xi6Ki23K24g/maxresdefault.jpg Data Science at Scale with Dask A Pythonic introduction to methods for scaling your data science and machine learning work to larger datasets and larger models with Dask, all while staying within the comfort of the tools and APIs you know and love from the PyData stack (such as numpy, pandas, and scikit-learn). We'll discuss: - How to reason about when you need to scale your data and machine learning work and when not to; - How to leverage distribute computation on your local workstation (such as your laptop) to analyze larger datasets and build larger, more complex models; - How to harness the power of clusters to support larger-than-memory computation, all from the comfort of your own laptop. https://www.youtube.com/watch?v=xi6Ki23K24g https://www.youtube-nocookie.com/embed/xi6Ki23K24g 2022-05-06 yes https://pycon.de/archive/2022/talks/deepdoctection-an-open-source-package-for-document-intelligence/ 2022-05-06 https://pycon.de/static/media/social/talks/HP9KVN.png deepdoctection - An open source package for document intelligence https://i.ytimg.com/vi/EXmJjpEQxrM/maxresdefault.jpg deepdoctection - An open source package for document intelligence Extracting information from business documents is difficult. They often have a complex visual structure and the information they contain is not tagged. Let me introduce deepdoctection: A tool box that is intended to facilitate entry into this topic. https://www.youtube.com/watch?v=EXmJjpEQxrM https://www.youtube-nocookie.com/embed/EXmJjpEQxrM 2022-05-06 yes https://pycon.de/archive/2022/talks/demystifying-python-s-internals-diving-into-cpython-by-implementing-a-pipe-operator/ 2022-01-07 https://pycon.de/static/media/social/talks/7ZWBFV.png Demystifying Python's Internals: Diving into CPython by implementing a pipe operator https://pycon.de/archive/2022/talks/detecting-drift-how-to-evaluate-and-explore-data-drift-in-machine-learning-systems/ 2022-05-06 https://pycon.de/static/media/social/talks/ASW8CJ.png Detecting drift: how to evaluate and explore data drift in machine learning systems https://i.ytimg.com/vi/0wdCMvU1vCQ/maxresdefault.jpg Detecting drift: how to evaluate and explore data drift in machine learning systems When your ML model is in production, you might observe input data and prediction drift. In absence of ground truth, drift can serve as a proxy for the model performance. But how exactly to evaluate it? In this talk, I will give an overview of the possible approaches, and how to implement and visualize the results. https://www.youtube.com/watch?v=0wdCMvU1vCQ https://www.youtube-nocookie.com/embed/0wdCMvU1vCQ 2022-05-06 yes https://pycon.de/archive/2022/talks/do-i-need-to-be-dr-frankenstein-to-create-real-ish-synthetic-data/ 2022-05-06 https://pycon.de/static/media/social/talks/3VAZ7R.png Do I need to be Dr. Frankenstein to create real-ish synthetic data? https://i.ytimg.com/vi/vqRxxuDu4kE/maxresdefault.jpg Do I need to be Dr. Frankenstein to create real-ish synthetic data? Synthetic datasets have caught the fancy of researchers, statisticians, and analysts. Also called fake or proxy data, not only does it address the privacy needs of the data subjects but also offers a workaround in case of unprecedented situations. Take the example of clinical data requirements during the SARS-Cov-2 pandemic. This talk introduces the concept of synthetic data to the audience who is curious about the hype surrounding it and see themselves using it in future. Apart from the appreciation of synthetic datasets and their different types, we would also see how the realness of such Frankenstein datasets is gauged. I would also discuss the options that are available for their generation, and how you do not need to be a mad scientist to make realistic synthetic datasets. https://www.youtube.com/watch?v=vqRxxuDu4kE https://www.youtube-nocookie.com/embed/vqRxxuDu4kE 2022-05-06 yes https://pycon.de/archive/2022/talks/do-we-really-need-data-scientists/ 2022-05-06 https://pycon.de/static/media/social/talks/BKSMFA.png Do we really need Data Scientists? https://i.ytimg.com/vi/PxCVbpDYaCM/maxresdefault.jpg Do we really need Data Scientists? The term Data Science has emerged in the late 70s in a wider spectrum of research and industry and since then we observed an increasing trend for it, especially from late 90s. Data Science job became very hot between 2010-2018 and many companies started hiring data scientists worldwide and many people have been changing their career to data science, from academia or IT industry. However, we have been hearing about “death of data science” here and there in the past 2 years as AI/ML field has been growing and many automated ML tools emerging. Looking at Google Trends for “Data Science” shows a slow decreasing pattern since mid 2020 as well. That means people have started to google less for “Data Science”. And the job descriptions have become more in the direction of Machine Learning Engineering for so many companies. The job definition of data science has always been vague, and any company has been adopting it to their own needs, but it might be now the time to properly define this job. In this talk I would like to talk about what the job of a data science is and if data science is really going to die and why there is a gap between data science job descriptions and the real skills of the workforce looking for the jobs. https://www.youtube.com/watch?v=PxCVbpDYaCM https://www.youtube-nocookie.com/embed/PxCVbpDYaCM 2022-05-06 yes https://pycon.de/archive/2022/talks/easily-build-interactive-plots-and-apps-with-hvplot/ 2022-05-06 https://pycon.de/static/media/social/talks/38HQZ3.png Easily build interactive plots and apps with hvPlot https://i.ytimg.com/vi/xlFMLQKZi3I/maxresdefault.jpg Easily build interactive plots and apps with hvPlot Do you use the `.plot()` API of pandas or xarray? Do you ever wish it was easier for you or your collaborators to try out different combinations of the parameters in your data-processing pipeline? This tutorial will introduce you to [hvPlot](https://hvplot.holoviz.org/), a library that: * supercharges the `.plot` API with extra capabilities like interactive plots, rendering of very large datasets, and simple composition and linking of plots, and * makes it really easy to build interactive web applications, whether to make data exploration easier for yourself or for sharing your results with others, by simply replacing arguments in your method calls with widgets. https://www.youtube.com/watch?v=xlFMLQKZi3I https://www.youtube-nocookie.com/embed/xlFMLQKZi3I 2022-05-06 yes https://pycon.de/archive/2022/talks/easy-and-flexible-imaging-with-the-core-imaging-library/ 2021-12-20 https://pycon.de/static/media/social/talks/GSLJUY.png Easy and flexible imaging with the Core Imaging Library https://pycon.de/archive/2022/talks/easy-python-lies-damned-lies-and-metaclasses/ 2022-05-06 https://pycon.de/static/media/social/talks/BG37DV.png "Easy Python": lies, damned lies, and metaclasses https://i.ytimg.com/vi/lRQgJh3Iu78/maxresdefault.jpg "Easy Python": lies, damned lies, and metaclasses We often refer to Python as a "simple, executable pseudo-code" language. Such statements appear in books, tutorials and all over the Internet. But after junior developers join their first full-time job they are welcomed by protocols, decorators, context managers, metaclasses and rest of the "real Python" shenanigans. During my talk I will cover top-10 Python complexities and how they are required to fight the "software complexity problem" in big projects. https://www.youtube.com/watch?v=lRQgJh3Iu78 https://www.youtube-nocookie.com/embed/lRQgJh3Iu78 2022-05-06 yes https://pycon.de/archive/2022/talks/efficient-data-labelling-with-weak-supervision/ 2022-05-06 https://pycon.de/static/media/social/talks/LAUL7F.png Efficient data labelling with weak supervision https://i.ytimg.com/vi/5NvSn4rel04/maxresdefault.jpg Efficient data labelling with weak supervision Data labelling is often considered a separate task that takes place before the real "machine learning work" happens, similar to waterfall software engineering practices. However this is typically a wrong approach that leads to failure of the whole project. In this talk, we will show how to use weak supervision techniques to not only label large amounts of data significantly faster than with other techniques, but to also protect your ML project from issues in the annotation step which can cause catastrophic errors further downstream. https://www.youtube.com/watch?v=5NvSn4rel04 https://www.youtube-nocookie.com/embed/5NvSn4rel04 2022-05-06 yes https://pycon.de/archive/2022/talks/fast-native-data-structures-c-c-from-python/ 2022-05-06 https://pycon.de/static/media/social/talks/7TCM8E.png Fast native data structures: C/C++ from Python https://i.ytimg.com/vi/E7-22Ml1wdY/maxresdefault.jpg Fast native data structures: C/C++ from Python This talk will show how compiling your Python code with Cython (https://cython.org/) enables you to make direct use of fast and memory efficient native data types and data structures. Cython provides very efficient ways to access the internals of Python data structures, process data from NumPy arrays, and use data structures from native C libraries or the C++ STL standard library as replacements for the high-level Python collections. https://www.youtube.com/watch?v=E7-22Ml1wdY https://www.youtube-nocookie.com/embed/E7-22Ml1wdY 2022-05-06 yes https://pycon.de/archive/2022/talks/faster-workflow-with-testdriven-development/ 2022-05-06 https://pycon.de/static/media/social/talks/ADRKQZ.png Faster Workflow with Testdriven Development https://i.ytimg.com/vi/_6AaG-cgMjk/maxresdefault.jpg Faster Workflow with Testdriven Development I will show how testdriven development will actually be an asset which can be less time consuming than the default approach for most developers. In this tutorial I will fail myself forward building up a fully functioning url shortener using FastAPI and pytest. https://www.youtube.com/watch?v=_6AaG-cgMjk https://www.youtube-nocookie.com/embed/_6AaG-cgMjk 2022-05-06 yes https://pycon.de/archive/2022/talks/financial-portfolio-management-with-deep-reinforcement-learning/ 2022-01-16 https://pycon.de/static/media/social/talks/MQ3XUB.png Financial Portfolio Management with Deep Reinforcement Learning https://pycon.de/archive/2022/talks/flexible-ml-experiment-tracking-system-for-python-coders-with-dvc-and-streamlit/ 2022-05-06 https://pycon.de/static/media/social/talks/WADNGC.png Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit https://i.ytimg.com/vi/YOSVMMwTlHM/maxresdefault.jpg Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit There are tons of tools to do data science. Too often, data scientists end up using a monolithic AI platform that “does everything by clicking on a UI”. In this talk, I will walk you through a pythonic and flexible ML experiment tracking system based on 1) GIT 🙂 2) [DVC](https://dvc.org/) (Data Version Control) to track the data 3) [Streamlit](https://streamlit.io/) to build a data exploration app. I will quickly introduce the tools separately and then focus on how they can be combined together to build a tailor-made experiment tracking system. https://www.youtube.com/watch?v=YOSVMMwTlHM https://www.youtube-nocookie.com/embed/YOSVMMwTlHM 2022-05-06 yes https://pycon.de/archive/2022/talks/forget-mono-vs-multi-repo-building-centralized-git-workflows-with-python/ 2022-05-06 https://pycon.de/static/media/social/talks/9ZVQE7.png Forget Mono vs. Multi-Repo - Building Centralized Git Workflows with Python https://i.ytimg.com/vi/Q-H5plbl-zU/maxresdefault.jpg Forget Mono vs. Multi-Repo - Building Centralized Git Workflows with Python The mono vs. multi-repo is an age-old debate in the DevOpsphere, and one that can still cause flame wars. What if I were to tell you that you don't have to choose? In this talk we will dive into how we built a centralized Git workflow that can work with any kind of repo architecture, delivered with Python. https://www.youtube.com/watch?v=Q-H5plbl-zU https://www.youtube-nocookie.com/embed/Q-H5plbl-zU 2022-05-06 yes https://pycon.de/archive/2022/talks/forget-web-3-0-let-s-talk-about-web-0-0-a-brief-history-of-the-internet-and-the-world-wide-web/ 2022-01-16 https://pycon.de/static/media/social/talks/T8Y38G.png Forget ‘web 3.0’, let's talk about ‘web 0.0’. A brief history of the Internet, and the World Wide Web. https://pycon.de/archive/2022/talks/fundamentals-of-relational-databases/ 2022-05-06 https://pycon.de/static/media/social/talks/KMFPAN.png Fundamentals of relational databases https://i.ytimg.com/vi/QQUpQ1tpF_o/maxresdefault.jpg Fundamentals of relational databases Are you somewhat comfortable with using SQL to access data, but are curious to know what happens behind the scenes when you send off your query? Then this is the talk for you! Relational database systems have been around since the 1970s and they are stacked full of cool ideas and concepts for making data handling fast and secure. And we are still using them, nearly unchanged, 50 years later! Reason enough, I think, to learn a bit more about how they work and the fundamentals of their design. Audience level: If you have played around with SQL / written some smaller queries, you’ll be just fine. https://www.youtube.com/watch?v=QQUpQ1tpF_o https://www.youtube-nocookie.com/embed/QQUpQ1tpF_o 2022-05-06 yes https://pycon.de/archive/2022/talks/grokking-lime-how-can-we-explain-why-an-image-classifier-knows-whats-in-a-photo-without-looking-inside-the-model/ 2022-01-10 https://pycon.de/static/media/social/talks/ZJUWZJ.png Grokking LIME: How can we explain why an image classifier "knows" what’s in a photo without looking inside the model? https://pycon.de/archive/2022/talks/honey-i-shrunk-the-target-variable-common-pitfalls-when-transforming-the-target-variable-and-how-to-exploit-transformations/ 2021-12-09 https://pycon.de/static/media/social/talks/7YDWYL.png Honey, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations. https://pycon.de/archive/2022/talks/how-a-simple-streamlit-dashboard-will-help-to-put-your-machine-learning-model-in-production/ 2022-01-16 https://pycon.de/static/media/social/talks/METVVV.png How a simple streamlit dashboard will help to put your machine learning model in production https://pycon.de/archive/2022/talks/how-to-build-a-python-based-research-cloud-platform-from-scratch/ 2022-05-06 https://pycon.de/static/media/social/talks/WWQGR8.png How to build a Python-based Research Cloud Platform from scratch https://i.ytimg.com/vi/a0KuuCwny3g/maxresdefault.jpg How to build a Python-based Research Cloud Platform from scratch This talk will present the journey of a quantitative asset manager from an outdated (non-Python) onPrem research setup to a modern Python-centric cloud research platform. We will examine the requirements and challenges associated with the project and present how we navigated finding the right solution for us among many alternatives. https://www.youtube.com/watch?v=a0KuuCwny3g https://www.youtube-nocookie.com/embed/a0KuuCwny3g 2022-05-06 yes https://pycon.de/archive/2022/talks/how-to-deal-with-toxic-people/ 2022-05-06 https://pycon.de/static/media/social/talks/WPR7DK.png How to deal with toxic people https://i.ytimg.com/vi/7lIpP3GEyXs/maxresdefault.jpg How to deal with toxic people As an open source maintainer, sooner or later you'll encounter ungrateful, entitled or outright toxic people who can be a real drain on your motivation and general mental health. In this session I'll first show some examples of such situations, then share some coping strategies that I've successfully used to deal with them. I'll also share some things that everyone can do to help maintainers and open source communities in responding to negativity. https://www.youtube.com/watch?v=7lIpP3GEyXs https://www.youtube-nocookie.com/embed/7lIpP3GEyXs 2022-05-06 yes https://pycon.de/archive/2022/talks/how-to-find-your-way-through-a-million-lines-of-code/ 2022-05-06 https://pycon.de/static/media/social/talks/WHVGAX.png How to Find Your Way Through a Million Lines of Code https://i.ytimg.com/vi/5nRW3KNgpls/maxresdefault.jpg How to Find Your Way Through a Million Lines of Code No matter whether you are starting a new job or you want to contribute to an open-source project, learning a new code base can be intimidating. This is especially true for code bases with more than one million lines of code. This talk will show you approaches, techniques and tools on how to master a new code base. https://www.youtube.com/watch?v=5nRW3KNgpls https://www.youtube-nocookie.com/embed/5nRW3KNgpls 2022-05-06 yes https://pycon.de/archive/2022/talks/how-to-trust-your-deep-learning-code/ 2022-05-06 https://pycon.de/static/media/social/talks/YQK7UU.png How to Trust Your Deep Learning Code https://i.ytimg.com/vi/ICKATFuYQTE/maxresdefault.jpg How to Trust Your Deep Learning Code Errors in Deep Learning are hard to catch as training often fails silently s. Unit testing can catch those errors early and lets you make changes to your code base with confidence. Learn about the unique challenges of testing Deep Learning systems and how to trust your code again with hands-on examples explained on a realistic Deep Learning repository. https://www.youtube.com/watch?v=ICKATFuYQTE https://www.youtube-nocookie.com/embed/ICKATFuYQTE 2022-05-06 yes https://pycon.de/archive/2022/talks/impact-of-cultivating-a-diverse-and-inclusive-workplace/ 2022-05-06 https://pycon.de/static/media/social/talks/T8D89Z.png Impact of Cultivating a Diverse and Inclusive Workplace https://i.ytimg.com/vi/jdM6VFjJYYM/maxresdefault.jpg Impact of Cultivating a Diverse and Inclusive Workplace Let’s face it. The positive impact of diversity and inclusion is no longer debatable. Although diversity and inclusion (D&I) offers clear benefits, it’s difficult to implement. A major issue is that many companies believe they’re already promoting a diverse and inclusive culture. However, only 40 percent of employees agree that their manager fosters an inclusive environment. A diverse and inclusive environment establishes a sense of belonging among employees. When employees feel more connected at work, they tend to work harder and smarter, producing higher quality work. As a result, organizations that adopt D&I practices see huge gains in the form of business results, innovation, and decision-making. https://www.youtube.com/watch?v=jdM6VFjJYYM https://www.youtube-nocookie.com/embed/jdM6VFjJYYM 2022-05-06 yes https://pycon.de/archive/2022/talks/inpsect-and-try-to-interpret-your-scikit-learn-machine-learning-models/ 2022-05-06 https://pycon.de/static/media/social/talks/9LZTRR.png Inpsect and try to interpret your scikit-learn machine-learning models https://i.ytimg.com/vi/V03NkNGEF3w/maxresdefault.jpg Inpsect and try to interpret your scikit-learn machine-learning models This tutorial presents the different inspection techniques currently available to inspect a machine-learning model developed with scikit-learn. In addition, we compare all these methods and pinpoint their limitations. https://www.youtube.com/watch?v=V03NkNGEF3w https://www.youtube-nocookie.com/embed/V03NkNGEF3w 2022-05-06 yes https://pycon.de/archive/2022/talks/introducing-the-dask-active-memory-manager/ 2022-05-06 https://pycon.de/static/media/social/talks/MZUDYP.png Introducing the Dask Active Memory Manager https://i.ytimg.com/vi/pYtjhhVLqFQ/maxresdefault.jpg Introducing the Dask Active Memory Manager The Active Memory Manager is a new experimental feature of Dask which aims to reduce the memory footprint of the cluster, prevent hard to debug out-of-memory issues, and make worker retirement more robust. https://www.youtube.com/watch?v=pYtjhhVLqFQ https://www.youtube-nocookie.com/embed/pYtjhhVLqFQ 2022-05-06 yes https://pycon.de/archive/2022/talks/introduction-to-mlops-with-mlflow/ 2022-05-06 https://pycon.de/static/media/social/talks/DV8PJT.png Introduction to MLOps with MLflow https://i.ytimg.com/vi/IUF4s9SXnd4/maxresdefault.jpg Introduction to MLOps with MLflow Machine learning requires experimenting with different datasets, data preparation steps, and algorithms to build a model that maximizes some target metric. Once you have built a model, you also need to deploy it to a production system, monitor its performance, and continuously retrain it on new data and compare with alternative models. A possible solution to managing parts of this complexity is offered by **MLFlow**. In this tutorial, you will learn how to use MLflow to: - _Set up_ a tracking server and a model repository. - _Keep track_ of machine learning training and experiment results (parameters, metrics and artifacts) with **MLflow Tracking**. - _Package_ the training code in a reusable and reproducible format with **MLFlow Projects**. - _Deploy_ the model into a HTTP server with **MLFlow Models** and keep track of it's state. https://www.youtube.com/watch?v=IUF4s9SXnd4 https://www.youtube-nocookie.com/embed/IUF4s9SXnd4 2022-05-06 yes https://pycon.de/archive/2022/talks/introduction-to-opc-ua-and-industrial-iot-liberate-machines-from-the-proprietary-clutches-of-big-hardware-with-the-power-of-opcua-asyncio/ 2021-12-13 https://pycon.de/static/media/social/talks/GMHRV9.png Introduction to OPC-UA and industrial IoT: Liberate machines from the proprietary clutches of Big Hardware with the power of opcua-asyncio https://pycon.de/archive/2022/talks/introduction-to-uplift-modeling/ 2022-05-06 https://pycon.de/static/media/social/talks/QY7P98.png Introduction to Uplift Modeling https://i.ytimg.com/vi/VWjsi-5yc3w/maxresdefault.jpg Introduction to Uplift Modeling In this talk we introduce uplift modelling, a method to estimate causal effects of a treatment, e.g. a marketing campaign, to effectively target customers that are most likely to respond to it. We describe the most common methods to estimate such effects by working a concrete example. https://www.youtube.com/watch?v=VWjsi-5yc3w https://www.youtube-nocookie.com/embed/VWjsi-5yc3w 2022-05-06 yes https://pycon.de/archive/2022/talks/it-is-all-about-files-and-http/ 2022-05-06 https://pycon.de/static/media/social/talks/SVZEXS.png It is all about files and HTTP https://i.ytimg.com/vi/U-2k0ovzAPg/maxresdefault.jpg It is all about files and HTTP Why do some .pdf files open in a browser at a lick? At the same time, others wait for the answer of where to download? I know that some pages load as fast as lightning. Besides, There are examples which just poodle along. In this presentation, The reasons and solutions will come up. https://www.youtube.com/watch?v=U-2k0ovzAPg https://www.youtube-nocookie.com/embed/U-2k0ovzAPg 2022-05-06 yes https://pycon.de/archive/2022/talks/jsonargparse-say-goodbye-to-configuration-hassles/ 2022-05-06 https://pycon.de/static/media/social/talks/XK73C3.png jsonargparse - Say goodbye to configuration hassles https://i.ytimg.com/vi/2gDf2S0nHKg/maxresdefault.jpg jsonargparse - Say goodbye to configuration hassles Did you ever find yourself in one of the following situations? * You hard-coded a parameter and now you really wish you could easily change it. * A proper command line interface would be nice, but you are way too lazy to write a lot of code for it. * You have a bunch of config files for different runs which only differ in a few entries. In this talk, you'll learn how to easily manage the situations described above using the open-source library [jsonargparse](https://github.com/omni-us/jsonargparse). If you are new to this topic, this talk can hopefully save you configuration-related frustrations in the future. If you are already using the likes of click, fire, typer or hydra, you'll get to know some of jsonargparse's advanced features that your current tool might be missing. https://www.youtube.com/watch?v=2gDf2S0nHKg https://www.youtube-nocookie.com/embed/2gDf2S0nHKg 2022-05-06 yes https://pycon.de/archive/2022/talks/jupyterlite-jupyter-webassembly-python/ 2022-05-06 https://pycon.de/static/media/social/talks/LSVVWT.png JupyterLite: Jupyter ❤️ WebAssembly ❤️ Python https://i.ytimg.com/vi/4rDRs_W9ICM/maxresdefault.jpg JupyterLite: Jupyter ❤️ WebAssembly ❤️ Python JupyterLite is a JupyterLab distribution that runs entirely in the web browser, backed by in-browser language kernels such as the WebAssembly powered Pyodide kernel. JupyterLite enables data science and interactive computing with the PyData scientific stack, directly in the browser, without installing anything or running a server. https://www.youtube.com/watch?v=4rDRs_W9ICM https://www.youtube-nocookie.com/embed/4rDRs_W9ICM 2022-05-06 yes https://pycon.de/archive/2022/talks/machine-learning-testing-ecosystem-of-python/ 2022-05-06 https://pycon.de/static/media/social/talks/9UB3Z3.png Machine Learning Testing Ecosystem of Python https://i.ytimg.com/vi/lffKmis16ic/maxresdefault.jpg Machine Learning Testing Ecosystem of Python In this talk, I'll present the growing ecosystem of machine learning testing tools in Python. Machine learning validation and testing is an emerging concern in the MLOps domain and will become more so in the near future as several states (including European Commission) will put regulations on AI in place. I'll talk about several types of machine learning vulnerabilities and the available toolkits in Python that help machine learning practitioners test their models. https://www.youtube.com/watch?v=lffKmis16ic https://www.youtube-nocookie.com/embed/lffKmis16ic 2022-05-06 yes https://pycon.de/archive/2022/talks/make-the-most-of-django/ 2022-05-06 https://pycon.de/static/media/social/talks/LWQFFK.png Make the most of Django https://i.ytimg.com/vi/Bz7_wuuU1_s/maxresdefault.jpg Make the most of Django Taking full advantage of **Open Source** software means getting involved in its **community** and **contributing** to its development. We will see how this is profoundly true in the **Django** case as well. https://www.youtube.com/watch?v=Bz7_wuuU1_s https://www.youtube-nocookie.com/embed/Bz7_wuuU1_s 2022-05-06 yes https://pycon.de/archive/2022/talks/making-machine-learning-applications-fast-and-simple-with-onnx/ 2021-11-30 https://pycon.de/static/media/social/talks/BLVRVL.png Making Machine Learning Applications Fast and Simple with ONNX https://pycon.de/archive/2022/talks/making-mlops-uncool-again/ 2022-05-06 https://pycon.de/static/media/social/talks/3PVNYH.png Making MLOps uncool again https://i.ytimg.com/vi/_kN3sDC3XKY/maxresdefault.jpg Making MLOps uncool again Machine learning operations (MLOps) have gained attention among practitioners aiming to automate the development of Machine Learning models, attempting to mimic the impact of DevOps in software. However, MLOps platforms are usually built isolated from the software development process, arguing that the well-proven tools used for DevOps can't be applied to Machine Learning projects. In this workshop, we will use [HuggingFace](https://huggingface.co/) to train a model that predicts labels for GitHub issues. By extending the power of Git and Github with [DVC](https://dvc.org/) and [CML](https://cml.dev/), our workflow will be able to handle the entire lifecycle of a Machine Learning model using the same tools and platforms that have been proven to work for software development. https://www.youtube.com/watch?v=_kN3sDC3XKY https://www.youtube-nocookie.com/embed/_kN3sDC3XKY 2022-05-06 yes https://pycon.de/archive/2022/talks/ml-communication-101-how-to-talk-about-machine-learning-with-anyone/ 2021-12-23 https://pycon.de/static/media/social/talks/JYSF3P.png ML Communication 101: How to talk about Machine Learning with anyone https://pycon.de/archive/2022/talks/my-forecast-is-better-than-yours-what-does-that-even-mean/ 2022-05-06 https://pycon.de/static/media/social/talks/WHHMWQ.png My forecast is better than yours! What does that even mean? https://i.ytimg.com/vi/ldzRfYeI6TA/maxresdefault.jpg My forecast is better than yours! What does that even mean? Forecasting is one of the most popular applications of Machine Learning. In the last decades, it went from large numbers, few factors, and simple algorithms to small numbers, many factors, and complex ML models. Moreover, some modern forecasting models can predict not only naive point estimators of the target variable but their probability distributions. As an example, BlueYonder delivers demand forecasts in the form of demand probability distribution on a very granular level (e.g. for each product, store, and day). However, established forecast evaluation procedures and criteria (e.g. directly using metrics like RMAE, RMSE, MAPE, etc., and comparing these metrics between various data categories) often turn out to be inappropriate and biased. Therefore, it is important to understand the limitations of the traditionally used metrics and approaches. BlueYonder has implemented forecast evaluation techniques to address these limitations. In this talk, I will present the most important issues in forecast evaluation and their possible resolutions based on the real use cases of demand forecasting developed within BlueYonder. https://www.youtube.com/watch?v=ldzRfYeI6TA https://www.youtube-nocookie.com/embed/ldzRfYeI6TA 2022-05-06 yes https://pycon.de/archive/2022/talks/navigating-the-limitations-of-pythons-concurrency-model-in-web-services/ 2022-05-06 https://pycon.de/static/media/social/talks/EQSRNN.png Navigating the limitations of Python’s concurrency model in web services https://i.ytimg.com/vi/Kf65EVT5Eyg/maxresdefault.jpg Navigating the limitations of Python’s concurrency model in web services How do web frameworks navigate the limitations of the Python concurrency model? In this talk we will dive into the inner workings of some of Python’s most commonly used frameworks. Then by looking into their underlying runtimes we will compare ASGIs and WSGIs. We will use FastAPI as a source of inspiration to see what modern web frameworks can offer and what are the tradeoffs with comparison to the more commonly used ones such as Flask. Finally we will get to mention alternatives with slightly different approaches in this space such as Tornado. https://www.youtube.com/watch?v=Kf65EVT5Eyg https://www.youtube-nocookie.com/embed/Kf65EVT5Eyg 2022-05-06 yes https://pycon.de/archive/2022/talks/on-blocks-copies-and-views-updating-pandas-internals/ 2022-05-06 https://pycon.de/static/media/social/talks/XJMXFK.png On Blocks, Copies and Views: updating pandas' internals https://i.ytimg.com/vi/aBeEN2klZQE/maxresdefault.jpg On Blocks, Copies and Views: updating pandas' internals a.k.a. “Getting rid of the SettingWithCopyWarning” Pandas’ current behavior on whether indexing returns a view or copy is confusing, even for experienced users. But it doesn’t have to be this way. We can make this aspect of pandas easier to grasp by simplifying the copy/view rules, and at the same time make pandas more memory-efficient. And get rid of the SettingWithCopyWarning. https://www.youtube.com/watch?v=aBeEN2klZQE https://www.youtube-nocookie.com/embed/aBeEN2klZQE 2022-05-06 yes https://pycon.de/archive/2022/talks/optimize-your-network-inference-time-with-openvino/ 2022-05-06 https://pycon.de/static/media/social/talks/PKERX8.png Optimize your network inference time with OpenVINO https://i.ytimg.com/vi/z2kjm3xbhbA/maxresdefault.jpg Optimize your network inference time with OpenVINO During the talk, I'll present the OpenVINO™ Toolkit. You'll learn how to automatically convert the model using Model Optimizer and how to run the inference with OpenVINO Runtime to infer your model with low latency on the CPU and iGPU you already have. The magic with only a few lines of code. https://www.youtube.com/watch?v=z2kjm3xbhbA https://www.youtube-nocookie.com/embed/z2kjm3xbhbA 2022-05-06 yes https://pycon.de/archive/2022/talks/overcoming-5-hurdles-to-using-jupyter-notebooks-for-data-science-by-the-jetbrains-datalore-team/ 2022-03-11 https://pycon.de/static/media/social/talks/9D9X8L.png Overcoming 5 Hurdles to Using Jupyter Notebooks for Data Science, by the JetBrains Datalore Team https://pycon.de/archive/2022/talks/performing-content-can-nlp-and-deep-learning-algorithms-predict-reader-preferences/ 2022-01-16 https://pycon.de/static/media/social/talks/XQMVKN.png Performing Content: Can NLP and Deep Learning algorithms predict reader preferences? https://pycon.de/archive/2022/talks/ppml-machine-learning-on-data-you-cannot-see/ 2022-05-06 https://pycon.de/static/media/social/talks/QHJ7SX.png PPML: Machine Learning on Data you cannot see https://i.ytimg.com/vi/gYKxQ6T8aH4/maxresdefault.jpg PPML: Machine Learning on Data you cannot see What if I tell you that you can run a complete ML pipeline on private data, without any anonymisation, nor even accessing the data in the first place? 🧐 And what If I also tell you that you can do that with no disruption to your existing pipeline, nor affecting the overall model performance? 😱 Well, that wouldn't be entirely true 😇 but in this workshop we'll explore the great potential **privacy-preserving machine learning** methods have to run _machine learning experiments on data you cannot see_. In the first part, we will first explore examples of exploits and vulnerabilities of Deep Learning models trained on anonymised data, whilst in the second part we will go much deeper into PPML methods, training DL on encrypted data, and more. You'll just have to be familiar with PyTorch, and DL basics to attend this workshop! https://www.youtube.com/watch?v=gYKxQ6T8aH4 https://www.youtube-nocookie.com/embed/gYKxQ6T8aH4 2022-05-06 yes https://pycon.de/archive/2022/talks/practical-graph-neural-networks-in-python-with-tensorflow-and-spektral/ 2022-05-06 https://pycon.de/static/media/social/talks/ZMFZUB.png Practical graph neural networks in Python with TensorFlow and Spektral https://i.ytimg.com/vi/hCY0_6etLjk/maxresdefault.jpg Practical graph neural networks in Python with TensorFlow and Spektral Graph neural networks (GNNs) have become one of the hottest research topics in recent years. Their popularity is reinforced by hugely successful industry applications in social networks, biology, chemistry, neuroscience and many other areas. One of the main challenges faced by data scientists and researchers who want to apply graph networks in their work is that they require different data structures and a slightly different training approach than traditional deep learning models. During the workshop we’ll demonstrate how to implement graph neural networks, how to prepare your data and – finally – how to train a GNN model for node-level and graph-level tasks using Spektral and TensorFlow. https://www.youtube.com/watch?v=hCY0_6etLjk https://www.youtube-nocookie.com/embed/hCY0_6etLjk 2022-05-06 yes https://pycon.de/archive/2022/talks/predictive-maintenance-and-anomaly-detection-for-wind-energy/ 2022-05-06 https://pycon.de/static/media/social/talks/3EZH9P.png Predictive Maintenance and Anomaly Detection for Wind Energy https://i.ytimg.com/vi/qCmnb_6ThpM/maxresdefault.jpg Predictive Maintenance and Anomaly Detection for Wind Energy This talk will describe predictive modeling applications in wind turbine maintenance, the challenges of anomaly detection and ways to move to more automatic diagnoses by modeling past documented defects. https://www.youtube.com/watch?v=qCmnb_6ThpM https://www.youtube-nocookie.com/embed/qCmnb_6ThpM 2022-05-06 yes https://pycon.de/archive/2022/talks/processing-open-street-map-data-with-python-and-postgresql/ 2022-05-06 https://pycon.de/static/media/social/talks/AKCLEP.png Processing Open Street Map Data with Python and PostgreSQL https://i.ytimg.com/vi/z9_prnssu0w/maxresdefault.jpg Processing Open Street Map Data with Python and PostgreSQL The Open Street Map (OSM) project is a global, open-source database with over 50GB of data, and this number grows everyday with every user submission. With such a big data set out there, also comes a huge potential for analysis and use in scientific studies. In this talk, you will learn how to get started with your own analysis using PostgreSQL as the data store and Python as the data processing language. To help you see exactly how this works, I first introduce the OSM data types, explain how to import this data into PostgreSQL and then cover how you can organise a Python project for data analysis. I end the talk by going over an example project, and there will also be plenty of links and resources for those wishing to learn even more. https://www.youtube.com/watch?v=z9_prnssu0w https://www.youtube-nocookie.com/embed/z9_prnssu0w 2022-05-06 yes https://pycon.de/archive/2022/talks/pytest-simple-rapid-and-fun-testing-with-python-3-hours/ 2022-05-06 https://pycon.de/static/media/social/talks/W93DBJ.png pytest - simple, rapid and fun testing with Python (3 hours) https://i.ytimg.com/vi/ofPHJrAOaTE/maxresdefault.jpg pytest - simple, rapid and fun testing with Python (3 hours) The pytest tool presents a rapid and simple way to write tests for your Python code. This training gives an introduction with exercises to some distinguishing features. We'll also examine how to run existing non-pytest test suites and discuss migration strategies. Various plugins which extend pytest's functionality even further will be introduced. https://www.youtube.com/watch?v=ofPHJrAOaTE https://www.youtube-nocookie.com/embed/ofPHJrAOaTE 2022-05-06 yes https://pycon.de/archive/2022/talks/python-3-10-welcome-to-pattern-matching/ 2022-05-06 https://pycon.de/static/media/social/talks/PYS7G8.png Python 3.10: Welcome to pattern matching! https://i.ytimg.com/vi/bBMX0ddR3XI/maxresdefault.jpg Python 3.10: Welcome to pattern matching! Python 3.10 is out and a new way of thinking about programming was brought to the Python language - Pattern Matching. We will go through how to use this feature in different scenarios and you will learn how simple, yet powerful Pattern Matching really is. This talk and you, it is a match! https://www.youtube.com/watch?v=bBMX0ddR3XI https://www.youtube-nocookie.com/embed/bBMX0ddR3XI 2022-05-06 yes https://pycon.de/archive/2022/talks/python-3-11-in-the-web-browser-a-journey/ 2022-05-06 https://pycon.de/static/media/social/talks/SBCNDY.png Python 3.11 in the Web Browser - A Journey https://i.ytimg.com/vi/oa2LllRZUlU/maxresdefault.jpg Python 3.11 in the Web Browser - A Journey Python 3.11 alpha comes with experimental support for Web Assembly and can be built to run in modern web browsers or Node.js out of the box. I’m going to show how we achieved the goal, which obstacles we faced, and what is missing to have fully working “Python for the web”. https://www.youtube.com/watch?v=oa2LllRZUlU https://www.youtube-nocookie.com/embed/oa2LllRZUlU 2022-05-06 yes https://pycon.de/archive/2022/talks/python-for-everyone-pyladies-insights-panel-discussion/ 2022-05-06 https://pycon.de/static/media/social/talks/U8WQMT.png Python for Everyone - PyLadies' Insights Panel Discussion https://i.ytimg.com/vi/6IKlIJdS11o/maxresdefault.jpg Python for Everyone - PyLadies' Insights Panel Discussion PyLadies Mission is to advance a diverse Python Community. This work is done by many volunteers in PyLadies chapters around the world, and here in Germany. And the benefits of this work are often felt far beyond the community itself. Pyladies Germany currently has four chapters: Berlin, Hamburg, Karlsruhe, and Munich. Each working to support underrepresented people in our community, so they can thrive. They work to create safe spaces for learning and networking. Join this panel to learn more about how these volunteers and organizers make a difference, what they would like the wider python community to understand, so they could be more effective in their work, and what you could do tomorrow, to help advance this work. https://www.youtube.com/watch?v=6IKlIJdS11o https://www.youtube-nocookie.com/embed/6IKlIJdS11o 2022-05-06 yes https://pycon.de/archive/2022/talks/python-m5p-m5-prime-regression-trees-in-python-compliant-with-scikit-learn/ 2022-05-06 https://pycon.de/static/media/social/talks/8KXYBD.png `python-m5p` - M5 Prime regression trees in python, compliant with scikit-learn https://i.ytimg.com/vi/KkEVD3JncdI/maxresdefault.jpg `python-m5p` - M5 Prime regression trees in python, compliant with scikit-learn Regression trees are powerful Machine Learning models capable of both flexibility in modeling as well as interpretability when the tree is not too deep. The M5 algorithm, introduced by Quinlan in 1992 to provide more compact and smooth models for regression, was improved by Wang & Witten in 1997, under the name M5 Prime (acronym M5' or M5P). The algorithm gained popularity in particular a dozen years later with the Weka Machine Learning toolbox, providing a java-based implementation. `python-m5p` is an implementation of the M5P algorithm compliant with scikit-learn. https://www.youtube.com/watch?v=KkEVD3JncdI https://www.youtube-nocookie.com/embed/KkEVD3JncdI 2022-05-06 yes https://pycon.de/archive/2022/talks/quitting-pip-how-we-use-git-submodules-to-manage-internal-dependencies-that-require-fast-iteration/ 2022-01-15 https://pycon.de/static/media/social/talks/B3HC8S.png Quitting pip: How we use git submodules to manage internal dependencies that require fast iteration https://pycon.de/archive/2022/talks/refactoring/ 2022-05-06 https://pycon.de/static/media/social/talks/UCMC3C.png Refactoring https://i.ytimg.com/vi/13hVzP3Oofs/maxresdefault.jpg Refactoring In this tutorial, you will refactor a space travel text adventure. Starting with a working but messy program, you will improve the structure of the code. You will identify redundant code segments, split long functions into shorter ones, extract data structures and encapsulate behavior into classes. The outcome will be a program that is more readable, easier to maintain, and, hopefully, still works. The tutorial will be delivered as a guided tour with many hands-on exercises. Together, we collect strategies that can be applied to Python projects that grow bigger and bigger. The refactoring tutorial is suitable for junior Python developers. https://www.youtube.com/watch?v=13hVzP3Oofs https://www.youtube-nocookie.com/embed/13hVzP3Oofs 2022-05-06 yes https://pycon.de/archive/2022/talks/reproducible-machine-learning-and-science-with-python/ 2022-05-06 https://pycon.de/static/media/social/talks/87BFX7.png Reproducible machine learning and science with python https://i.ytimg.com/vi/0ShpadTHsro/maxresdefault.jpg Reproducible machine learning and science with python With machine learning being used in all domains of science, reproducibility and openness is major concern for these experiments and workflows, This tutorial will discuss various experiment tracking tools and focus on OpenML for dataset, model, run, and benchmark reproducibility(via openml-python). https://www.youtube.com/watch?v=0ShpadTHsro https://www.youtube-nocookie.com/embed/0ShpadTHsro 2022-05-06 yes https://pycon.de/archive/2022/talks/rewriting-your-r-analysis-code-in-python/ 2022-05-06 https://pycon.de/static/media/social/talks/ZEA9NJ.png Rewriting your R analysis code in Python https://i.ytimg.com/vi/Es8HGYVweU0/maxresdefault.jpg Rewriting your R analysis code in Python R and Python are two of the most powerful tools for any kind of data analysis. But both programming languages have their strengths and weaknesses. This means it can be necessary to switch from one to the other. When is it a good idea to rewrite your R code in Python? And what are good tools to do that successfully? What pitfalls are there? https://www.youtube.com/watch?v=Es8HGYVweU0 https://www.youtube-nocookie.com/embed/Es8HGYVweU0 2022-05-06 yes https://pycon.de/archive/2022/talks/sankey-plots-with-python/ 2022-05-06 https://pycon.de/static/media/social/talks/7SFQNW.png Sankey Plots with Python https://i.ytimg.com/vi/rgwr3wDQ3uQ/maxresdefault.jpg Sankey Plots with Python This talk provides an introduction to Sankey plots with a focus on creating them with various Python libraries. We will talk about the pros and cons of the libraries, give practical advice on how and when to use them, and what you should regard when creating Sankey plots yourself. https://www.youtube.com/watch?v=rgwr3wDQ3uQ https://www.youtube-nocookie.com/embed/rgwr3wDQ3uQ 2022-05-06 yes https://pycon.de/archive/2022/talks/secure-ml-automated-security-best-practices-in-machine-learning/ 2022-05-06 https://pycon.de/static/media/social/talks/APTWQS.png Secure ML: Automated Security Best Practices in Machine Learning https://i.ytimg.com/vi/82uiA5evtyU/maxresdefault.jpg Secure ML: Automated Security Best Practices in Machine Learning As data science capabilities scale, the core concept of security becomes growingly critical. In this talk we will introduce the security challenges and solutions for data science practitioners relevant to each of the phases of the machine learning lifecycle, and we will provide a practical set of best practices and frameworks that can be adopted to ensure a relevant level of security is present in the multiple stages of the machine learning lifecycle. https://www.youtube.com/watch?v=82uiA5evtyU https://www.youtube-nocookie.com/embed/82uiA5evtyU 2022-05-06 yes https://pycon.de/archive/2022/talks/securing-django-applications/ 2022-05-06 https://pycon.de/static/media/social/talks/8MWCQG.png Securing Django Applications https://i.ytimg.com/vi/d0vzeb_viwU/maxresdefault.jpg Securing Django Applications Django is the most popular Python-based web framework used for creating web applications. The web applications are vulnerable for various reasons including a) configuration settings of the web applications b) lack of implementation of security best practices and secure coding and c) lack of awareness of secure first web applications among developers. The vulnerable web applications put the data of the customers at greater risk and the compromised code can lead to problems beyond control. It is very important to develop secure web applications to protect customer data and code to mitigate the risk. In this talk, we will focus on two aspects. First, performing penetration testing on Django web applications to identify vulnerabilities and scanning for Open Web Application Security Project (OWASP) Top 10 risks. Second, strategies and configuration settings for making the source code and Django applications secure. We will also discuss the Djangohunter tool to identify incorrectly configured Django applications that are exposing sensitive information. https://www.youtube.com/watch?v=d0vzeb_viwU https://www.youtube-nocookie.com/embed/d0vzeb_viwU 2022-05-06 yes https://pycon.de/archive/2022/talks/seeing-the-needle-and-the-haystack-single-datapoint-selection-for-billion-point-datasets/ 2022-03-12 https://pycon.de/static/media/social/talks/9LDACE.png Seeing the needle AND the haystack: single-datapoint selection for billion-point datasets https://pycon.de/archive/2022/talks/serious-time-for-time-series/ 2022-05-06 https://pycon.de/static/media/social/talks/DTTQ9D.png (Serious) Time for Time Series https://i.ytimg.com/vi/2b725bplNt8/maxresdefault.jpg (Serious) Time for Time Series From inventory to website visitors, resource planning to financial data, time-series data is all around us. Knowing what comes next is key to success in this dynamically changing world. And for that we need reliable forecasting models. While complex & deep models may be good at forecasting, they typically give us little insight about the underlying patterns in our data. Such insights however may be a key to not only forecasting the future but shaping it. In this tutorial, we'll cover relatively simple approaches for time series analysis and seasonality modelling with Pandas. https://www.youtube.com/watch?v=2b725bplNt8 https://www.youtube-nocookie.com/embed/2b725bplNt8 2022-05-06 yes https://pycon.de/archive/2022/talks/sktime-python-toolbox-for-time-series-advanced-forecasting-probabilistic-global-and-hierarchical/ 2022-01-16 https://pycon.de/static/media/social/talks/GCYTM3.png sktime - python toolbox for time series: advanced forecasting - probabilistic, global and hierarchical https://pycon.de/archive/2022/talks/slack-bots-101-an-introduction-into-slack-bot-based-workflow-automation/ 2022-05-06 https://pycon.de/static/media/social/talks/SFDVVA.png Slack bots 101: An introduction into slack bot-based workflow automation https://i.ytimg.com/vi/jB5LGEjFVvU/maxresdefault.jpg Slack bots 101: An introduction into slack bot-based workflow automation Most developers work with Slack every day, yet very few of them know about the awesome things you can do when you build your own slack bot. For example, recently, we built a slack bot that automates our hiring assessment sending process. During this talk, we will discuss the lessons we learned during the creation of this slack bot, and we will teach you to build and deploy your first bot. https://www.youtube.com/watch?v=jB5LGEjFVvU https://www.youtube-nocookie.com/embed/jB5LGEjFVvU 2022-05-06 yes https://pycon.de/archive/2022/talks/speeding-up-python-with-zig/ 2022-05-06 https://pycon.de/static/media/social/talks/DFWSQR.png Speeding up Python with Zig https://i.ytimg.com/vi/O0MmmZxdct4/maxresdefault.jpg Speeding up Python with Zig Zig is an almost drop-in replacement for C, and that includes directly importing and calling the Python C API without the use of FFI and/or bindings. This makes it an alternative candidate for implementing libraries that require C-like performance. https://www.youtube.com/watch?v=O0MmmZxdct4 https://www.youtube-nocookie.com/embed/O0MmmZxdct4 2022-05-06 yes https://pycon.de/archive/2022/talks/squirrel-efficient-data-loading-for-large-scale-deep-learning/ 2022-05-06 https://pycon.de/static/media/social/talks/CHTY3U.png Squirrel - Efficient Data Loading for Large-Scale Deep Learning https://i.ytimg.com/vi/pZPbi4EmqEo/maxresdefault.jpg Squirrel - Efficient Data Loading for Large-Scale Deep Learning Data stall in deep learning training refers to the case where combined throughput of data loading and transformation is less than the consumption rate of the model, leading to idling of expensive GPU resources and prolonged training. Data loading in deep learning pipelines have a very specific set of constraints, performance requirements, and cost structure. While Object Store is a low-cost storage solution, repeated retrieval can be expensive and slow, which can lead to data stall. SSD is an expensive storage solution with fast retrieval, which is not as scalable as Object Store. Run-time transformation is a common subsequent step, which is highly variable across model configurations, and highly dependent on the data loading step. Any configuration of data loading which is optimal in one scenario is almost certainly sub-optimal in another. Therefore, an ideal data pipeline should be elastic and adaptable. We present solutions to these challenges. Our approach uses chain-able components to express the deep learning data pipeline with pluggable executors to decouple IO-bound and CPU-bound operations, and to scale out to clusters of machines. We discuss the importance of sharding and caching for cost reduction, and the unification of storage and loading based on open standard file formats. We hope that our efforts make large-scale model training accessible to a wider community of researchers and practitioners, and enable sustainable deep learning pipelines. https://www.youtube.com/watch?v=pZPbi4EmqEo https://www.youtube-nocookie.com/embed/pZPbi4EmqEo 2022-05-06 yes https://pycon.de/archive/2022/talks/stupid-things-i-ve-done-with-python/ 2022-05-06 https://pycon.de/static/media/social/talks/N9RV8L.png Stupid Things I've Done With Python https://i.ytimg.com/vi/_MDksZlA9Bo/maxresdefault.jpg Stupid Things I've Done With Python On every computer I've had for the past 20 years, I've created a folder called "stupid python tricks". It's where I put code that should never see the light of day. Code that abuses advanced features like decorators, metaclasses, and dynamic typing to do terrible things. Code I'm going to teach you. https://www.youtube.com/watch?v=_MDksZlA9Bo https://www.youtube-nocookie.com/embed/_MDksZlA9Bo 2022-05-06 yes https://pycon.de/archive/2022/talks/the-magic-of-python-objects/ 2021-12-24 https://pycon.de/static/media/social/talks/GCAKXD.png The Magic of Python Objects https://pycon.de/archive/2022/talks/the-myth-of-neutrality-how-ai-is-widening-social-divides/ 2022-05-06 https://pycon.de/static/media/social/talks/ML7XNX.png The Myth of Neutrality: How AI is widening social divides https://i.ytimg.com/vi/Q5P4elINDws/maxresdefault.jpg The Myth of Neutrality: How AI is widening social divides Many people expect artificial intelligence to be neutral - or at least more objective than we humans are. But is it really? In recent years, researchers and activists have shown that it is in fact not, and that our biases end up becoming part of AI systems. My talk will shed light on how algorithms become discriminatory, how difficult it is to build "fair and responsible" AI, and what we should do to prevent the systems we build from cementing existing injustices. https://www.youtube.com/watch?v=Q5P4elINDws https://www.youtube-nocookie.com/embed/Q5P4elINDws 2022-05-06 yes https://pycon.de/archive/2022/talks/the-secret-sauce-of-data-science-management/ 2022-05-06 https://pycon.de/static/media/social/talks/LWUWAU.png The secret sauce of data science management https://i.ytimg.com/vi/tbBfVHIh-38/maxresdefault.jpg The secret sauce of data science management The question - “how to become a successful data scientist?” is often discussed in conferences like these. Today I would like to address a second level question. How to make the success scale? How to build a DS team in which the whole is greater than the sum of its parts? The question - “how to become a successful data scientist?” is often discussed in conferences like these. Today I would like to address a second level question. How to make the success scale? How to build a DS team in which the whole is greater than the sum of its parts? In this talk, I will share lessons learned during my 4 years as data science team & group leader on how to build a DS team that prospers while addressing the unique challenges of leading such a team. We will discuss how to create the setting for a DS to grow and research while driving winning results for the organization. I will share our AI playbook that enables us to select the right projects to maximize our value and to collaborate with our partners most effectively to bring them to production. In addition we will discuss the importance of communication with upper management and how to do it right. https://www.youtube.com/watch?v=tbBfVHIh-38 https://www.youtube-nocookie.com/embed/tbBfVHIh-38 2022-05-06 yes https://pycon.de/archive/2022/talks/the-state-of-devops-for-python-projects/ 2022-05-06 https://pycon.de/static/media/social/talks/CEUZ87.png The state of DevOps for Python projects https://i.ytimg.com/vi/CedUm-1N7P8/maxresdefault.jpg The state of DevOps for Python projects Let's take a look at the DevOps space through the eyes of a team of Python developers. Where does it stand today and where are we going? This talk showcases our approach of implementing DevOps techniques to streamline and accelerate our daily development. We will look at a number of real-world examples and best practices taken straight from the pipelines used at alcemy to release code several times a day. https://www.youtube.com/watch?v=CedUm-1N7P8 https://www.youtube-nocookie.com/embed/CedUm-1N7P8 2022-05-06 yes https://pycon.de/archive/2022/talks/there-are-python-2-relics-in-your-code/ 2022-05-06 https://pycon.de/static/media/social/talks/NEEA8M.png There Are Python 2 Relics in Your Code https://i.ytimg.com/vi/o-tpiZV90Tc/maxresdefault.jpg There Are Python 2 Relics in Your Code Are you done with the migration to Python 3 or did you involuntarily leave some Python 2 relics in your code? Did you leave it as a backdoor so you can easily return to Python 2 if we find out that the whole Python 3 was a dead-end street? https://www.youtube.com/watch?v=o-tpiZV90Tc https://www.youtube-nocookie.com/embed/o-tpiZV90Tc 2022-05-06 yes https://pycon.de/archive/2022/talks/transformer-based-clustering-identifying-product-clusters-for-e-commerce/ 2022-01-14 https://pycon.de/static/media/social/talks/URDTCT.png Transformer based clustering: Identifying product clusters for E-commerce https://pycon.de/archive/2022/talks/trojan-source-malware-can-we-trust-open-source-anymore/ 2022-05-06 https://pycon.de/static/media/social/talks/HYGPW9.png Trojan Source Malware - Can we trust open-source anymore? https://i.ytimg.com/vi/cm5lI5u9-eE/maxresdefault.jpg Trojan Source Malware - Can we trust open-source anymore? Recently, a paper is published to demonstrate how a visibly valid contribution can contain malicious code by exporting the Unicode control characters. Some of these attacks has been tested on Python and it works. Shall the Python and open-source communities be concerned? https://www.youtube.com/watch?v=cm5lI5u9-eE https://www.youtube-nocookie.com/embed/cm5lI5u9-eE 2022-05-06 yes https://pycon.de/archive/2022/talks/unclear-code-hurts/ 2022-05-06 https://pycon.de/static/media/social/talks/GSG7KQ.png Unclear Code Hurts https://i.ytimg.com/vi/CkeuNL8LAC0/maxresdefault.jpg Unclear Code Hurts Code may work or not, but it will always tell a story. Computers will not complain about how you write it (except correct syntax), but human readers will. This talk is about writing clear code and caring for the human beings that will read it. Yourself included. https://www.youtube.com/watch?v=CkeuNL8LAC0 https://www.youtube-nocookie.com/embed/CkeuNL8LAC0 2022-05-06 yes https://pycon.de/archive/2022/talks/unsupervised-shallow-learning-for-fraud-detection-on-marketplaces/ 2022-05-06 https://pycon.de/static/media/social/talks/GCVHBH.png Unsupervised shallow learning for fraud detection on marketplaces https://i.ytimg.com/vi/Ce_IPb7htGY/maxresdefault.jpg Unsupervised shallow learning for fraud detection on marketplaces Combating fraud, scams and wrongdoings in large marketplaces and platforms that connect millions of individuals as sellers and shoppers poses a very exciting and also difficult problem. Adyen leverages massive transaction information to solve this problem for platforms such as eBay or GoFundMe. In this talk we'll cover how we defined the problem, iterated on it and leveraged open source data tooling over python (airflow, spark, tensorflow, keras) and shallow unsupervised learning to solve it. https://www.youtube.com/watch?v=Ce_IPb7htGY https://www.youtube-nocookie.com/embed/Ce_IPb7htGY 2022-05-06 yes https://pycon.de/archive/2022/talks/upgrade-your-documentation-to-the-next-level/ 2022-01-10 https://pycon.de/static/media/social/talks/3BYTZU.png Upgrade your Documentation to the Next Level https://pycon.de/archive/2022/talks/using-a-database-in-a-data-science-project-lessons-learned-in-production/ 2022-05-06 https://pycon.de/static/media/social/talks/VUHBWP.png Using a database in a data science project - Lessons learned in production https://i.ytimg.com/vi/x0R5_GvKTvc/maxresdefault.jpg Using a database in a data science project - Lessons learned in production Storing and processing data in a relational database for a machine learning project presents unique challenges. Processing large volumes can take long, source data has to be continuously ingested and kept up to date, the schema needs to change over time while the application is running daily. The amount of available tools and options can be confusing. In this talk, we'll present the solutions and tricks we developed in four years operating a machine learning project in production. https://www.youtube.com/watch?v=x0R5_GvKTvc https://www.youtube-nocookie.com/embed/x0R5_GvKTvc 2022-05-06 yes https://pycon.de/archive/2022/talks/we-know-what-your-app-did-last-summer-do-you-observing-python-applications-using-prometheus/ 2022-01-14 https://pycon.de/static/media/social/talks/RE89WX.png We know what your app did last summer. Do you? Observing Python applications using Prometheus. https://pycon.de/archive/2022/talks/web-based-live-visualisation-of-sensor-data/ 2022-05-06 https://pycon.de/static/media/social/talks/MMP3US.png Web based live visualisation of sensor data https://i.ytimg.com/vi/o_cr-RmGmio/maxresdefault.jpg Web based live visualisation of sensor data Raspberry Pis are great for interfacing directly with sensor hardware. However, users might prefer their familiar desktop computer or any mobile device to watch the measured data instantly. This is where a Python-based web interface helps to circumvent most platform-specific issues. The presentation is aimed at users with limited experience in APIs and web interfaces. An example application based on Redis, FastAPI and visualization frameworks like plotly.js or three.js will be introduced. The audience may interact via WLAN with the live demo. https://www.youtube.com/watch?v=o_cr-RmGmio https://www.youtube-nocookie.com/embed/o_cr-RmGmio 2022-05-06 yes https://pycon.de/archive/2022/talks/what-are-data-unit-tests-and-why-we-need-them/ 2022-05-06 https://pycon.de/static/media/social/talks/MPWLWP.png What are data unit tests and why we need them https://i.ytimg.com/vi/Kshch2G8AB4/maxresdefault.jpg What are data unit tests and why we need them I will introduce the concept of data unit tests and why they are important in the workflow of data scientists when building data products. In this talk, you will learn a new tool you can use to ensure the quality of the products you build. https://www.youtube.com/watch?v=Kshch2G8AB4 https://www.youtube-nocookie.com/embed/Kshch2G8AB4 2022-05-06 yes https://pycon.de/archive/2022/talks/what-i-learned-from-monitoring-more-than-30-machine-learning-use-cases/ 2022-05-06 https://pycon.de/static/media/social/talks/SEXPKA.png What I learned from monitoring more than 30 Machine Learning Use Cases https://i.ytimg.com/vi/wWxqnZb-LSk/maxresdefault.jpg What I learned from monitoring more than 30 Machine Learning Use Cases This talk summarizes the most important insights I gained from running more than 30 machine learning use cases in production. We will take a loan prediction model as an example use case and cover questions like: - What is the difference between metrics for model training and metrics for model monitoring? - Which metrics are generally useful to be monitored? - Which metrics should you prioritize? - How can monitoring be set up using a traditional software monitoring stack (tools like Grafana and Prometheus)? This talk will be useful for you if you: - are a hands-on engineer or data scientist - want to use your team's or company's existing monitoring and dashboarding infrastructure to monitor machine learning stacks - are a beginner or intermediate in MLOPs https://www.youtube.com/watch?v=wWxqnZb-LSk https://www.youtube-nocookie.com/embed/wWxqnZb-LSk 2022-05-06 yes https://pycon.de/archive/2022/talks/xai-meets-natural-language-processing/ 2022-05-06 https://pycon.de/static/media/social/talks/F3SZL3.png XAI meets Natural Language Processing https://i.ytimg.com/vi/66A5D6NU17U/maxresdefault.jpg XAI meets Natural Language Processing The idea of Explainable AI (XAI) gets more and more attention, as customers and users want to understand AI models and the reason behind predictions. But it is difficult to apply "traditional" XAI approaches out of the box to Natural Language Processing/Understanding models. For human viewers, sentences lose their meaning when turned into numbers and vectors, words become irrelevant when they appear midst of 1000 others. In this talk, I will show you different solution options and approaches, alternatives, and lessons learned based on a real-world NLP use case. https://www.youtube.com/watch?v=66A5D6NU17U https://www.youtube-nocookie.com/embed/66A5D6NU17U 2022-05-06 yes https://pycon.de/archive/2022/talks/you-shall-not-share/ 2022-05-06 https://pycon.de/static/media/social/talks/KG3LKN.png You shall not share! https://i.ytimg.com/vi/v2ZucdZj3bY/maxresdefault.jpg You shall not share! Online social network users frequently share personal information online. While each post is targeted to a certain audience, it is not always easy to judge what the privacy implications of shared content will be. To ensure that privacy is preserved, each user has to think through these implications before sharing content, which is difficult at best. Recent work advocates the use of intelligent systems that can help people preserve their privacy by helping users decide whether a content is private or not so that the user can take an action accordingly; e.g., only share with family as opposed to publicly. In this talk, I propose an agent that helps its user to determine the privacy of content she is willing to share. The agent uses only the content that the user has shared before, without discriminating between the content modality (e.g., image, text, and so on). Each content in the system is only represented with tags. The tags can be automatically created using a tool such as Clarifai, where 20 tags would automatically be assigned to an image. Alternatively, the user might herself choose to tag the content. This enables our approach to make use of content from different online social networks as long as tags are associated with the content. The agent learns the privacy label of a content using random forests, a well-known machine learning technique. The features are extracted using the Term Frequency–Relevance Frequency (TF-RF) method. https://www.youtube.com/watch?v=v2ZucdZj3bY https://www.youtube-nocookie.com/embed/v2ZucdZj3bY 2022-05-06 yes https://pycon.de/archive/2022/talks/your-data-your-insights-creating-personal-data-projects-to-re-own-the-data-you-share/ 2021-12-23 https://pycon.de/static/media/social/talks/UMN7FB.png Your data, your insights: creating personal data projects to (re-)own the data you share https://pycon.de/archive/2023/talks/5-things-about-fastapi-i-wish-we-had-known-beforehand/ 2023-06-18 https://pycon.de/static/media/social/talks/GBYWCY.png 5 Things about fastAPI I wish we had known beforehand https://i.ytimg.com/vi/KNeHNEs_I3k/maxresdefault.jpg 5 Things about fastAPI I wish we had known beforehand An exchange of views on fastAPI in practice. FastAPI is great, it helps many developers create REST APIs based on the OpenAPI standard and run them asynchronously. It has a thriving community and educational documentation. FastAPI does a great job of getting people started with APIs quickly. This talk will point out some obstacles and dark spots that I wish we had known about before. In this talk we want to highlight solutions. https://www.youtube.com/watch?v=KNeHNEs_I3k https://www.youtube-nocookie.com/embed/KNeHNEs_I3k 2023-06-18 yes https://pycon.de/archive/2023/talks/a-concrete-guide-to-time-series-databases-with-python/ 2023-06-18 https://pycon.de/static/media/social/talks/8WXSR9.png A concrete guide to time-series databases with Python https://i.ytimg.com/vi/1_SCktDB2eU/maxresdefault.jpg A concrete guide to time-series databases with Python We evaluated time-series databases and complementary services to stream-process sensor data. In this talk, our evaluation will be presented. The final implementation will be shown, alongside python-tools we’ve built and lessons learned during the process. https://www.youtube.com/watch?v=1_SCktDB2eU https://www.youtube-nocookie.com/embed/1_SCktDB2eU 2023-06-18 yes https://pycon.de/archive/2023/talks/accelerate-python-with-julia/ 2023-01-04 https://pycon.de/static/media/social/talks/UQ3KXD.png Accelerate Python with Julia https://pycon.de/archive/2023/talks/accelerating-public-consultations-with-large-language-models-a-case-study-from-the-uk-planning-inspectorate/ 2023-01-05 https://pycon.de/static/media/social/talks/HMGCPL.png Accelerating Public Consultations with Large Language Models: A Case Study from the UK Planning Inspectorate https://pycon.de/archive/2023/talks/accelerating-python-code/ 2023-06-18 https://pycon.de/static/media/social/talks/W9HLK3.png Accelerating Python Code https://i.ytimg.com/vi/HfQkY1gv2es/maxresdefault.jpg Accelerating Python Code Python is a beautiful language for fast prototyping and and sketching ideas quickly. People often struggle to get their code into production though for various reasons. Besides of all security and safety concerns that usually are not addressed from the very beginning when playing around with an algorithmic idea, performance concerns are quite frequently a reason for not taking the Python code to the next level. We will look at the "missing performance" worries using a simple numerical problem and how to speed the corresponding Python code up to top notch performance. https://www.youtube.com/watch?v=HfQkY1gv2es https://www.youtube-nocookie.com/embed/HfQkY1gv2es 2023-06-18 yes https://pycon.de/archive/2023/talks/actionable-machine-learning-in-the-browser-with-pyscript/ 2023-06-18 https://pycon.de/static/media/social/talks/9Q38VT.png Actionable Machine Learning in the Browser with PyScript https://i.ytimg.com/vi/e0OhG01efHo/maxresdefault.jpg Actionable Machine Learning in the Browser with PyScript PyScript brings the full PyData stack in the browser, opening up to unprecedented use cases for interactive data-intensive applications. In this scenario, the web browser becomes a ubiquitous computing platform, operating within a (nearly) _zero-installation_ & _server-less_ environment. In this talk, we will explore how to create full-fledged interactive front-end machine learning applications using PyScript. We will dive into the the main features of the PyScript platform (e.g. _built-in Javascript integration_ and _local modules_ ), discussing _new_ data & design patterns (e.g. _loading heterogeneous data in the browser_), required to adapt and to overcome the limitations imposed by the new operating environment (i.e. the browser). https://www.youtube.com/watch?v=e0OhG01efHo https://www.youtube-nocookie.com/embed/e0OhG01efHo 2023-06-18 yes https://pycon.de/archive/2023/talks/advanced-visual-search-engine-with-self-supervised-learning-ssl-representations-and-milvus/ 2023-01-04 https://pycon.de/static/media/social/talks/TCWCVV.png Advanced Visual Search Engine with Self-Supervised Learning (SSL) Representations and Milvus https://pycon.de/archive/2023/talks/an-unbiased-evaluation-of-environment-management-and-packaging-tools/ 2023-06-18 https://pycon.de/static/media/social/talks/VBP3PE.png An unbiased evaluation of environment management and packaging tools https://i.ytimg.com/vi/MsJjzVIVs6M/maxresdefault.jpg An unbiased evaluation of environment management and packaging tools Python packaging is quickly evolving and new tools pop up on a regular basis. Lots of talks and posts on packaging exist but none of them give a structured, unbiased overview of the available tools. This talk will shed light on the jungle of packaging and environment management tools, comparing them on a basis of predefined features. https://www.youtube.com/watch?v=MsJjzVIVs6M https://www.youtube-nocookie.com/embed/MsJjzVIVs6M 2023-06-18 yes https://pycon.de/archive/2023/talks/apache-arrow-connecting-and-accelerating-dataframe-libraries-across-the-pydata-ecosystem/ 2023-01-05 https://pycon.de/static/media/social/talks/H7ZCWK.png Apache Arrow: connecting and accelerating dataframe libraries across the PyData ecosystem https://pycon.de/archive/2023/talks/apache-streampipes-for-pythonistas-iiot-data-handling-made-easy/ 2023-06-18 https://pycon.de/static/media/social/talks/LXBGZS.png Apache StreamPipes for Pythonistas: IIoT data handling made easy! https://i.ytimg.com/vi/buAukOE8oEY/maxresdefault.jpg Apache StreamPipes for Pythonistas: IIoT data handling made easy! The industrial environment offers a lot of interesting use cases for data enthusiasts. There are myriads of interesting challenges that can be solved by data scientists. However, collecting industrial data in general and industrial IoT (IIoT) data in particular, is cumbersome and not really appealing for anyone who just wants to work with data. Apache StreamPipes addresses this pitfall and allows anyone to extract data from IIoT data sources without messing around with (old-fashioned) protocols. In addition, StreamPipes newly developed Python client now gives Pythonistas the ability to programmatically access and work with them in a Pythonic way. This talk will provide a basic introduction into the functionality of Apache StreamPipes itself, followed by a deeper discussion of the Python client. Finally, a live demo will show how IIoT data can be easily derived in Python and used directly for visualization and ML model training. https://www.youtube.com/watch?v=buAukOE8oEY https://www.youtube-nocookie.com/embed/buAukOE8oEY 2023-06-18 yes https://pycon.de/archive/2023/talks/ask-a-question-an-faq-answering-service-for-when-there-s-little-to-no-data/ 2023-06-18 https://pycon.de/static/media/social/talks/PEQZTC.png Ask-A-Question: an FAQ-answering service for when there's little to no data https://i.ytimg.com/vi/GoFRwLBNhjA/maxresdefault.jpg Ask-A-Question: an FAQ-answering service for when there's little to no data Doing data science in international development often means finding the right-sized solution in resource-constrained settings. This talk walks you through how my team helped answer thousands of questions from pregnant folks and new parents on a South African maternal and child health helpline, which model we ended up choosing and why (hint: resource-constraints!), and how we've packaged everything into a service that anyone can start for themselves, By the end of the talk, I hope you'll know how to start your own FAQ-answering service and learn about one example of doing data science in international development. https://www.youtube.com/watch?v=GoFRwLBNhjA https://www.youtube-nocookie.com/embed/GoFRwLBNhjA 2023-06-18 yes https://pycon.de/archive/2023/talks/aspect-oriented-programming-diving-deep-into-decorators/ 2023-06-18 https://pycon.de/static/media/social/talks/PVJMWB.png Aspect-oriented Programming - Diving deep into Decorators https://i.ytimg.com/vi/BunOdvHCEGw/maxresdefault.jpg Aspect-oriented Programming - Diving deep into Decorators The aspect-oriented programming paradigm can support the separation of cross-cutting concerns such as logging, caching, or checking of permissions. This can improve code modularity and maintainability. Python offers decorator to implement re-usable code for cross-cutting task. This tutorial is an in-depth introduction to decorators. It covers the usage of decorators and how to implement simple and more advanced decorators. Use cases demonstrate how to work with decorators. In addition to showing how functions can use closures to create decorators, the tutorial introduces callable class instance as alternative. Class decorators can solve problems that use be to be tasks for metaclasses. The tutorial provides uses cases for class decorators. While the focus is on best practices and practical applications, the tutorial also provides deeper insight into how Python works behind the scene. After the tutorial participants will feel comfortable with functions that take functions and return new functions. https://www.youtube.com/watch?v=BunOdvHCEGw https://www.youtube-nocookie.com/embed/BunOdvHCEGw 2023-06-18 yes https://pycon.de/archive/2023/talks/autogluon-automl-for-tabular-multimodal-and-time-series-data/ 2023-06-18 https://pycon.de/static/media/social/talks/WMAXSV.png AutoGluon: AutoML for Tabular, Multimodal and Time Series Data https://i.ytimg.com/vi/Lwu15m5mmbs/maxresdefault.jpg AutoGluon: AutoML for Tabular, Multimodal and Time Series Data AutoML, or automated machine learning, offers the promise of transforming raw data into accurate predictions with minimal human intervention, expertise, and manual experimentation. In this talk, we will introduce AutoGluon, a cutting-edge toolkit that enables AutoML for tabular, multimodal and time series data. AutoGluon emphasizes usability, enabling a wide variety of tasks from regression to time series forecasting and image classification through a unified and intuitive API. We will specifically focus on tasks on tabular and time series tasks where AutoGluon is the current state-of-the-art, and demonstrate how AutoGluon can be used to achieve competitive performance on tabular and time series competition data sets. We will also discuss the techniques used to automatically build and train these models, peeking under the hood of AutoGluon. https://www.youtube.com/watch?v=Lwu15m5mmbs https://www.youtube-nocookie.com/embed/Lwu15m5mmbs 2023-06-18 yes https://pycon.de/archive/2023/talks/bayesian-marketing-science-solving-marketing-s-3-biggest-problems/ 2023-06-18 https://pycon.de/static/media/social/talks/AXMS87.png Bayesian Marketing Science: Solving Marketing's 3 Biggest Problems https://i.ytimg.com/vi/RY-M0tvN77s/maxresdefault.jpg Bayesian Marketing Science: Solving Marketing's 3 Biggest Problems In this talk I will present two new open-source packages that make up a powerful and state-of-the-art marketing analytics toolbox. Specifically, PyMC-Marketing is a new library built on top of the popular Bayesian modeling library PyMC. PyMC-Marketing allows robust estimation of customer acquisition costs (via media mix modeling) as well as customer lifetime value. In addition, I will show how we can estimate the effectiveness of marketing campaigns using a new Bayesian causal inference package called CausalPy. The talk will be applied with a real-world case-study and many code examples. Special emphasis will be placed on the interplay between these tools and how they can be combined together to make optimal marketing budget decisions in complex scenarios. https://www.youtube.com/watch?v=RY-M0tvN77s https://www.youtube-nocookie.com/embed/RY-M0tvN77s 2023-06-18 yes https://pycon.de/archive/2023/talks/behind-the-scenes-of-tox-the-journey-of-rewriting-a-python-tool-with-more-than-10-million-monthly-downloads/ 2023-01-05 https://pycon.de/static/media/social/talks/XEVGVJ.png Behind the Scenes of tox: The Journey of Rewriting a Python Tool with more than 10 Million Monthly Downloads https://pycon.de/archive/2023/talks/bhad-explainable-unsupervised-anomaly-detection-using-bayesian-histograms/ 2023-06-18 https://pycon.de/static/media/social/talks/HNKMMP.png BHAD: Explainable unsupervised anomaly detection using Bayesian histograms https://i.ytimg.com/vi/_8zfgPTD-d8/maxresdefault.jpg BHAD: Explainable unsupervised anomaly detection using Bayesian histograms The detection of outliers or anomalous data patterns is one of the most prominent machine learning use cases in industrial applications. I present a Bayesian histogram anomaly detector (BHAD), where the number of bins is treated as an additional unknown model parameter with an assigned prior distribution. BHAD scales linearly with the sample size and enables a straightforward explanation of individual scores, which makes it very suitable for industrial applications when model interpretability is crucial. I study the predictive performance of the proposed BHAD algorithm with various SoA anomaly detection approaches using simulated data and also using popular benchmark datasets for outlier detection. The reported results indicate that BHAD has very competitive predictive accuracy compared to other more complex and computationally more expensive algorithms, while being explainable and fast. https://www.youtube.com/watch?v=_8zfgPTD-d8 https://www.youtube-nocookie.com/embed/_8zfgPTD-d8 2023-06-18 yes https://pycon.de/archive/2023/talks/ble-and-python-how-to-build-a-simple-ble-project-on-linux-with-python/ 2023-06-18 https://pycon.de/static/media/social/talks/WLMDZ7.png BLE and Python: How to build a simple BLE project on Linux with Python https://i.ytimg.com/vi/a77soe8oFVI/maxresdefault.jpg BLE and Python: How to build a simple BLE project on Linux with Python Bluetooth Low Energy (BLE) is a part of the Bluetooth standard aimed at bringing wireless technology to low-power devices, and it's getting into everything - lightbulbs, robots, personal health and fitness devices, and plenty more. One of the main advantages of BLE is that everybody can integrate those devices into their tools or projects. However, BLE is not the most developer-friendly protocol and these devices most of the time don't come with good documentation. In addition, there are not a lot of good open-source tools, examples, and tutorials on how to use Python with BLE. Especially if one wants to build both sides of the communication. In this talk, I will introduce the concepts and properties used in BLE interactions and look at how we can use the Linux Bluetooth Stack (Bluez) to communicate with other devices. We will look at a simple example and learn along the way about common pitfalls and debugging options while working with BLE and Python. This talk is for everybody that has a basic understanding of Python and wants to have a deeper understanding of how BLE works and how one could use it in a private project. https://www.youtube.com/watch?v=a77soe8oFVI https://www.youtube-nocookie.com/embed/a77soe8oFVI 2023-06-18 yes https://pycon.de/archive/2023/talks/bringing-nlp-to-production-an-end-to-end-story-about-some-multi-language-nlp-services/ 2023-01-04 https://pycon.de/static/media/social/talks/VHNJ37.png Bringing NLP to Production (an end to end story about some multi-language NLP services) https://pycon.de/archive/2023/talks/building-a-personal-assistant-with-gpt-and-haystack-how-to-feed-facts-to-large-language-models-and-reduce-hallucination/ 2023-01-05 https://pycon.de/static/media/social/talks/H8KMTT.png Building a Personal Assistant With GPT and Haystack: How to Feed Facts to Large Language Models and Reduce Hallucination. https://pycon.de/archive/2023/talks/building-hexagonal-python-services/ 2023-06-18 https://pycon.de/static/media/social/talks/EAKYPL.png Building Hexagonal Python Services https://i.ytimg.com/vi/qCw0ySOeekA/maxresdefault.jpg Building Hexagonal Python Services The importance of enterprise architecture patterns is all well-known and applicable to varied types of tasks. Thinking about the architecture from the beginning of the journey is crucial to have a maintainable, therefore testable, and flexible code base. In We are going to explore the Ports and Adapters(Hexagonal) pattern by showing a simple web app using Repository, Unit of Work, and Services(Use Cases) patterns tied together with Dependency Injection. All those patterns are quite famous in other languages but they are relatively new for the Python ecosystem, which is a crucial missing part. As a web framework, we are going to use FastAPI which can be replaced with any framework in a matter of time because of the abstractions we have added. https://www.youtube.com/watch?v=qCw0ySOeekA https://www.youtube-nocookie.com/embed/qCw0ySOeekA 2023-06-18 yes https://pycon.de/archive/2023/talks/cloud-infrastructure-from-python-code-how-far-could-we-go/ 2023-06-18 https://pycon.de/static/media/social/talks/RQ3MWN.png Cloud Infrastructure From Python Code: How Far Could We Go? https://i.ytimg.com/vi/l2YrsDmeRl0/maxresdefault.jpg Cloud Infrastructure From Python Code: How Far Could We Go? Discover how Infrastructure From Code (IfC) can revolutionize Cloud DevOps automation by generating cloud deployment templates directly from Python code. Learn how this technology empowers Python developers to easily deploy and operate cost-effective, secure, reliable, and sustainable cloud software. Join us to explore the strategic potential of IfC. https://www.youtube.com/watch?v=l2YrsDmeRl0 https://www.youtube-nocookie.com/embed/l2YrsDmeRl0 2023-06-18 yes https://pycon.de/archive/2023/talks/code-cleanup-a-data-scientist-s-guide-to-sparkling-code/ 2023-06-18 https://pycon.de/static/media/social/talks/MBZJE9.png Code Cleanup: A Data Scientist's Guide to Sparkling Code https://i.ytimg.com/vi/GxsNL_1lN_Y/maxresdefault.jpg Code Cleanup: A Data Scientist's Guide to Sparkling Code Does your production code look like it’s been copied from Untitled12.ipynb? Are your engineers complaining about the code but you can’t find the time to work on improving the code base? This talk will go through some of the basics of clean coding and how to best implement them in a data science team. https://www.youtube.com/watch?v=GxsNL_1lN_Y https://www.youtube-nocookie.com/embed/GxsNL_1lN_Y 2023-06-18 yes https://pycon.de/archive/2023/talks/common-issues-with-time-series-data-and-how-to-solve-them/ 2023-06-18 https://pycon.de/static/media/social/talks/ZRAFKA.png Common issues with Time Series data and how to solve them https://i.ytimg.com/vi/sSF1uzK6DuI/maxresdefault.jpg Common issues with Time Series data and how to solve them Time-series data is all around us: from logistics to digital marketing, from pricing to stock markets. It’s hard to imagine a modern business that has no time series data to forecast. However, mastering such forecasting is not an easy task. For this talk, together with other domain experts, I have collected a list of common time series issues that data professionals commonly run into. After this talk, you will learn to identify, understand, and resolve such issues. This will include stabilising divergent time series, organising delayed / irregular data, handling missing values without anomaly propagation, and reducing the impact of noise and outliers on your forecasting models. https://www.youtube.com/watch?v=sSF1uzK6DuI https://www.youtube-nocookie.com/embed/sSF1uzK6DuI 2023-06-18 yes https://pycon.de/archive/2023/talks/contributing-to-an-open-source-content-library-for-nlp/ 2023-06-18 https://pycon.de/static/media/social/talks/MECPWF.png Contributing to an open-source content library for NLP https://i.ytimg.com/vi/4JlKh3NeKv0/maxresdefault.jpg Contributing to an open-source content library for NLP Bricks is an open-source content library for natural language processing, which provides the building blocks to quickly and easily enrich, transform or analyze text data for machine learning projects. For many Pythonistas, contributing to an open-source project seems scary and intimidating. In this tutorial, we offer a hands-on experience in which programmers and data scientists learn how to code their own building blocks and share their creations with the community with ease. https://www.youtube.com/watch?v=4JlKh3NeKv0 https://www.youtube-nocookie.com/embed/4JlKh3NeKv0 2023-06-18 yes https://pycon.de/archive/2023/talks/cooking-up-a-ml-platform-growing-pains-and-lessons-learned/ 2023-06-18 https://pycon.de/static/media/social/talks/KMGYZF.png Cooking up a ML Platform: Growing pains and lessons learned https://i.ytimg.com/vi/g9WS920Y1fY/maxresdefault.jpg Cooking up a ML Platform: Growing pains and lessons learned What is a ML platform and do you even need one? When should you consider investing in your own ML platform? What challenges can you expect building and maintaining one? Tune in and discover (some) answers to these questions and more! I will share a first-hand account of our ongoing journey towards becoming a ML platform team within Delivery Hero's Logistics department, including how we got here, how we structure our work, and what challenges and tools we are focussing on next. https://www.youtube.com/watch?v=g9WS920Y1fY https://www.youtube-nocookie.com/embed/g9WS920Y1fY 2023-06-18 yes https://pycon.de/archive/2023/talks/create-interactive-jupyter-websites-with-jupyterlite/ 2023-06-18 https://pycon.de/static/media/social/talks/FZY9VV.png Create interactive Jupyter websites with JupyterLite https://i.ytimg.com/vi/WXRslU9D3bo/maxresdefault.jpg Create interactive Jupyter websites with JupyterLite Jupyter notebooks are a popular tool for data science and scientific computing, allowing users to mix code, text, and multimedia in a single document. However, sharing Jupyter notebooks can be challenging, as they require installing a specific software environment to be viewed and executed. JupyterLite is a Jupyter distribution that runs entirely in the web browser without any server components. A significant benefit of this approach is the ease of deployment. With JupyterLite, the only requirement to provide a live computing environment is a collection of static assets. In this talk, we will show how you can create such static website and deploy it to your users. https://www.youtube.com/watch?v=WXRslU9D3bo https://www.youtube-nocookie.com/embed/WXRslU9D3bo 2023-06-18 yes https://pycon.de/archive/2023/talks/data-driven-design-for-the-dask-scheduler/ 2023-06-18 https://pycon.de/static/media/social/talks/Q9GVEK.png Data-driven design for the Dask scheduler https://i.ytimg.com/vi/niioB7W5o-I/maxresdefault.jpg Data-driven design for the Dask scheduler Historically, changes in the scheduling algorithm of Dask have often been based on theory, single use cases, or even gut feeling. Coiled has now moved to using hard, comprehensive performance metrics for all changes - and it's been a turning point! https://www.youtube.com/watch?v=niioB7W5o-I https://www.youtube-nocookie.com/embed/niioB7W5o-I 2023-06-18 yes https://pycon.de/archive/2023/talks/data-kata-ensemble-programming-with-pydantic-1/ 2023-02-22 https://pycon.de/static/media/social/talks/VFZ3VT.png Data Kata: Ensemble programming with Pydantic #1 https://pycon.de/archive/2023/talks/data-kata-ensemble-programming-with-pydantic-2/ 2023-02-22 https://pycon.de/static/media/social/talks/DEQM3J.png Data Kata: Ensemble programming with Pydantic #2 https://pycon.de/archive/2023/talks/delivering-ai-at-scale/ 2023-06-18 https://pycon.de/static/media/social/talks/DTBTVF.png Delivering AI at Scale https://i.ytimg.com/vi/0F1cE8L-neE/maxresdefault.jpg Delivering AI at Scale Everybody knows our yellow vans, trucks and planes around the world. But do you know how data drives our business and how we leverage algorithms and technology in our core operations? We will share some “behind the scenes” insights on Deutsche Post DHL Group’s journey towards a Data-Driven Company. • Large-Scale Use Cases: Challenging and high impact Use Cases in all major areas of logistics, including Computer Vision and NLP • Fancy Algorithms: Deep-Neural Networks, TSP Solvers and the standard toolkit of a Data Scientist • Modern Tooling: Cloud Platforms, Kubernetes , Kubeflow, Auto ML • No rusty working mode: small, self-organized, agile project teams, combining state of the art Machine Learning with MLOps best practices • A young, motivated and international team – German skills are only “nice to have” But we have more to offer than slides filled with buzzwords. We will demonstrate our passion for our work, deep dive into our largest use cases that impact your everyday life and share our approach for a timeseries forecasting library - combining data science, software engineering and technology for efficient and easy to maintain machine learning projects.. https://www.youtube.com/watch?v=0F1cE8L-neE https://www.youtube-nocookie.com/embed/0F1cE8L-neE 2023-06-18 yes https://pycon.de/archive/2023/talks/driving-down-the-memray-lane-profiling-your-data-science-work/ 2023-06-18 https://pycon.de/static/media/social/talks/WHACAT.png Driving down the Memray lane - Profiling your data science work https://i.ytimg.com/vi/oOJRr_XYjUo/maxresdefault.jpg Driving down the Memray lane - Profiling your data science work When handling a large amount of data, memory profiling the data science workflow becomes more important. It gives you insight into which process consumes lots of memory. In this talk, we will introduce Mamray, a Python memory profiling tool and its new Jupyter plugin. https://www.youtube.com/watch?v=oOJRr_XYjUo https://www.youtube-nocookie.com/embed/oOJRr_XYjUo 2023-06-18 yes https://pycon.de/archive/2023/talks/dynamic-pricing-at-flix/ 2023-06-18 https://pycon.de/static/media/social/talks/33XCUR.png Dynamic pricing at Flix https://i.ytimg.com/vi/4KcTVbUHe70/maxresdefault.jpg Dynamic pricing at Flix In the talk we give a brief overview of how we use Dynamic Pricing to tune the prices for rides based on demand, time of purchase, unexpected events strike etc., and other criteria to fulfil our business requirements. https://www.youtube.com/watch?v=4KcTVbUHe70 https://www.youtube-nocookie.com/embed/4KcTVbUHe70 2023-06-18 yes https://pycon.de/archive/2023/talks/enabling-machine-learning-how-to-optimize-infrastructure-tools-and-teams-for-ml-workflows/ 2023-04-05 https://pycon.de/static/media/social/talks/WZWXLF.png Enabling Machine Learning: How to Optimize Infrastructure, Tools and Teams for ML Workflows https://pycon.de/archive/2023/talks/evosax-jax-based-evolution-strategies/ 2023-06-18 https://pycon.de/static/media/social/talks/WMWZQC.png evosax: JAX-Based Evolution Strategies https://i.ytimg.com/vi/OZgnAApYltU/maxresdefault.jpg evosax: JAX-Based Evolution Strategies Tired of having to handle asynchronous processes for neuroevolution? Do you want to leverage massive vectorization and high-throughput accelerators for evolution strategies (ES)? [evosax](https://github.com/RobertTLange/evosax) allows you to leverage JAX, XLA compilation and auto-vectorization/parallelization to scale ES to your favorite accelerators. In this talk we will get to know the core API and how to solve distributed black-box optimization problems with evolution strategies. https://www.youtube.com/watch?v=OZgnAApYltU https://www.youtube-nocookie.com/embed/OZgnAApYltU 2023-06-18 yes https://pycon.de/archive/2023/talks/exploring-the-power-of-cyclic-boosting-a-pure-python-explainable-and-efficient-ml-method/ 2023-02-06 https://pycon.de/static/media/social/talks/MYARJG.png Exploring the Power of Cyclic Boosting: A Pure-Python, Explainable, and Efficient ML Method https://pycon.de/archive/2023/talks/fastapi-and-celery-building-reliable-web-applications-with-tdd/ 2023-06-18 https://pycon.de/static/media/social/talks/8CVQDW.png FastAPI and Celery: Building Reliable Web Applications with TDD https://i.ytimg.com/vi/-3vlsnFDbd8/maxresdefault.jpg FastAPI and Celery: Building Reliable Web Applications with TDD In this talk, we will explore how to use the FastAPI web framework and Celery task queue to build reliable and scalable web applications in a test-driven manner. We will start by setting up a testing environment and writing unit tests for the core functionality of our application. Next, we will use FastAPI to create an api to perform some long-running task. Finally, we will then see how Celery can help us offload long-running tasks and improve the performance of our application. By the end of this talk, attendees will have a strong understanding of TDD and how to apply it to your FastAPI and Celery projects, and you will be able to write tests that ensure the reliability and maintainability of your code. https://www.youtube.com/watch?v=-3vlsnFDbd8 https://www.youtube-nocookie.com/embed/-3vlsnFDbd8 2023-06-18 yes https://pycon.de/archive/2023/talks/fear-the-mutants-love-the-mutants/ 2023-06-18 https://pycon.de/static/media/social/talks/AQAJDH.png Fear the mutants. Love the mutants. https://i.ytimg.com/vi/-5SuFClW21k/maxresdefault.jpg Fear the mutants. Love the mutants. Developers often use code coverage as a target, which makes it a bad measure of test quality. Mutation testing changes the game: create mutant versions of your code that break your tests, and you'll quickly start to write better tests! Come and learn to use it as part of your CI/CD process. I promise, you'll never look at penguins the same way again! https://www.youtube.com/watch?v=-5SuFClW21k https://www.youtube-nocookie.com/embed/-5SuFClW21k 2023-06-18 yes https://pycon.de/archive/2023/talks/from-notebook-to-pipeline-in-no-time-with-lineapy/ 2023-06-18 https://pycon.de/static/media/social/talks/WAVRYZ.png From notebook to pipeline in no time with LineaPy https://i.ytimg.com/vi/qcP5wNvV1uk/maxresdefault.jpg From notebook to pipeline in no time with LineaPy The nightmare before data science production: You found a working prototype for your problem using a Jupyter notebook and now it's time to build a production grade solution from that notebook. Unfortunately, your notebook looks anything but production grade. The good news is, there's finally a cure! The open-source python package LineaPy aims to automate data science workflow generation and expediting the process of going from data science development to production. And truly, it transforms messy notebooks into data pipelines like Apache Airflow, DVC, Argo, Kubeflow, and many more. And if you can't find your favorite orchestration framework, you are welcome to work with the creators of LineaPy to contribute a plugin for it! In this talk, you will learn the basic concepts of LineaPy and how it supports your everyday tasks as a data practitioner. For this purpose, we will transform a notebook step by step together to create a DVC pipeline. Finally, we will discuss what place LineaPy will take in the MLOps universe. Will you only have to check in your notebook in the future? https://www.youtube.com/watch?v=qcP5wNvV1uk https://www.youtube-nocookie.com/embed/qcP5wNvV1uk 2023-06-18 yes https://pycon.de/archive/2023/talks/geospatial-data-processing-with-python-a-comprehensive-tutorial/ 2023-06-18 https://pycon.de/static/media/social/talks/RPMMKZ.png Geospatial Data Processing with Python: A Comprehensive Tutorial https://i.ytimg.com/vi/6xDtxz_RnP0/maxresdefault.jpg Geospatial Data Processing with Python: A Comprehensive Tutorial In this tutorial, you will learn about the various Python modules for processing geospatial data, including GDAL, Rasterio, Pyproj, Shapely, Folium, Fiona, OSMnx, Libpysal, Geopandas, Pydeck, Whitebox, ESDA, and Leaflet. You will gain hands-on experience working with real-world geospatial data and learn how to perform tasks such as reading and writing spatial data, reprojecting data, performing spatial analyses, and creating interactive maps. This tutorial is suitable for beginners as well as intermediate Python users who want to expand their knowledge in the field of geospatial data processing https://www.youtube.com/watch?v=6xDtxz_RnP0 https://www.youtube-nocookie.com/embed/6xDtxz_RnP0 2023-06-18 yes https://pycon.de/archive/2023/talks/getting-started-with-jax/ 2023-06-18 https://pycon.de/static/media/social/talks/TWPBZF.png Getting started with JAX https://i.ytimg.com/vi/yDYiiVVkBXY/maxresdefault.jpg Getting started with JAX Deepminds JAX ecosystem provides deep learning practitioners with an appealing alternative to TensorFlow and PyTorch. Among its strengths are great functionalities such as native TPU support, as well as easy vectorization and parallelization. Nevertheless, making your first steps in JAX can feel complicated given some of its idiosyncrasies. This talk helps new users getting started in this promising ecosystem by sharing practical tips and best practises. https://www.youtube.com/watch?v=yDYiiVVkBXY https://www.youtube-nocookie.com/embed/yDYiiVVkBXY 2023-06-18 yes https://pycon.de/archive/2023/talks/giving-and-receiving-great-feedback-through-prs/ 2023-06-18 https://pycon.de/static/media/social/talks/9SENVW.png Giving and Receiving Great Feedback through PRs https://i.ytimg.com/vi/vAoeeqVdTBc/maxresdefault.jpg Giving and Receiving Great Feedback through PRs Do you struggle with PRs? Have you ever had to change code even though you disagreed with the change just to land the PR? Have you ever given feedback that would have improved the code only to get into a comment war? We'll discuss how to give and receive feedback to extract maximum value from it and avoid all the communication problems that come with PRs. https://www.youtube.com/watch?v=vAoeeqVdTBc https://www.youtube-nocookie.com/embed/vAoeeqVdTBc 2023-06-18 yes https://pycon.de/archive/2023/talks/great-security-is-one-question-away/ 2023-06-18 https://pycon.de/static/media/social/talks/MTRFT3.png Great Security Is One Question Away https://i.ytimg.com/vi/Hg5-bGbMzFo/maxresdefault.jpg Great Security Is One Question Away After a decade of writing code, I joined the application security team. During the transition process, I discovered that there are many myths about security, and how difficult it is. Often devs choose to ignore it because they think that writing more secure code would take them ages. It is not true. Security doesn’t have to be scary. From my talk, you will learn the most useful piece from the Application Security theory. It will be practical and not boring at all. https://www.youtube.com/watch?v=Hg5-bGbMzFo https://www.youtube-nocookie.com/embed/Hg5-bGbMzFo 2023-06-18 yes https://pycon.de/archive/2023/talks/grokking-anchors-uncovering-what-a-machine-learning-model-relies-on/ 2023-01-05 https://pycon.de/static/media/social/talks/QUAXG3.png Grokking Anchors: Uncovering What a Machine-Learning Model Relies On https://pycon.de/archive/2023/talks/have-your-cake-and-eat-it-too-rapid-model-development-and-stable-high-performance-deployments/ 2023-02-28 https://pycon.de/static/media/social/talks/ABNXHC.png Have your cake and eat it too: Rapid model development and stable, high-performance deployments https://pycon.de/archive/2023/talks/haystack-for-climate-q-a/ 2023-06-18 https://pycon.de/static/media/social/talks/SVXFP8.png Haystack for climate Q/A https://i.ytimg.com/vi/whh3xlPdYks/maxresdefault.jpg Haystack for climate Q/A How can NLP and Haystack help answer sustainability questions and fight climate change? In this talk we walkthrough our experience using Haystack to build Question Answering Models for the climate change and sustainability domain. We discuss how we did it, some of the challenges we faced, and what we learnt along the way! https://www.youtube.com/watch?v=whh3xlPdYks https://www.youtube-nocookie.com/embed/whh3xlPdYks 2023-06-18 yes https://pycon.de/archive/2023/talks/honey-i-broke-the-pytorch-model-debugging-custom-pytorch-models-in-a-structured-manner/ 2023-01-05 https://pycon.de/static/media/social/talks/GXAKV8.png Honey, I broke the PyTorch model >.< - Debugging custom PyTorch models in a structured manner https://pycon.de/archive/2023/talks/how-chatbots-work-we-need-to-talk/ 2023-06-18 https://pycon.de/static/media/social/talks/AKXXQD.png How Chatbots work – We need to talk! https://i.ytimg.com/vi/qipgC8tT-FU/maxresdefault.jpg How Chatbots work – We need to talk! Chatbots are fun to use, ranging from simple chit-chat (“How are you today?”) to more sophisticated use cases like shopping assistants, or the diagnosis of technical or medical problems. Despite their mostly simple user interaction, chatbots must combine various complex NLP concepts to deliver convincing, intelligent, or even witty results. With the advancing development of machine learning models and the availability of open source frameworks and libraries, chatbots are becoming more powerful every day and at the same time easier to implement. Yet, depending on the concrete use case, the implementation must be approached in specific ways. In the design process of chatbots it is crucial to define the language processing tasks thoroughly and to choose from a variety of techniques wisely. In this talk, we will look together at common concepts and techniques in modern chatbot implementation as well as practical experiences from an E-mobility bot that was developed using the Rasa framework. https://www.youtube.com/watch?v=qipgC8tT-FU https://www.youtube-nocookie.com/embed/qipgC8tT-FU 2023-06-18 yes https://pycon.de/archive/2023/talks/how-python-enables-future-computer-chips/ 2023-06-18 https://pycon.de/static/media/social/talks/VRGANP.png How Python enables future computer chips https://i.ytimg.com/vi/0soZf5v6NsA/maxresdefault.jpg How Python enables future computer chips At the semiconductor division of Carl Zeiss it's our mission to continuously make computer chips faster and more energy efficient. To do so, we go to the very limits of what is possible, both physically and technologically. This is only possible through massive research and development efforts. In this talk, we tell the story how Python became a central tool for our R&D activities. This includes technical aspects as well as organization and culture. How do you make sure that hundreds of people work in consistent environments? – How do you get all people on board to work together with Python? – You have lots of domain experts without much software background. How do you prevent them from creating a mess when projects get larger? https://www.youtube.com/watch?v=0soZf5v6NsA https://www.youtube-nocookie.com/embed/0soZf5v6NsA 2023-06-18 yes https://pycon.de/archive/2023/talks/how-to-baseline-in-nlp-and-where-to-go-from-there/ 2023-06-18 https://pycon.de/static/media/social/talks/B7PCUR.png How to baseline in NLP and where to go from there https://i.ytimg.com/vi/oc_p0ZgoyA8/maxresdefault.jpg How to baseline in NLP and where to go from there In this talk, we will explore the build-measure-learn paradigm and the role of baselines in natural language processing (NLP). We will cover the common NLP tasks of classification, clustering, search, and named entity recognition, and describe the baseline approaches that can be used for each task. We will also discuss how to move beyond these baselines through weak learning and transfer learning. By the end of this talk, attendees will have a better understanding of how to establish and improve upon baselines in NLP. https://www.youtube.com/watch?v=oc_p0ZgoyA8 https://www.youtube-nocookie.com/embed/oc_p0ZgoyA8 2023-06-18 yes https://pycon.de/archive/2023/talks/how-to-build-observability-into-a-ml-platform/ 2023-06-18 https://pycon.de/static/media/social/talks/TGZFSF.png How to build observability into a ML Platform https://i.ytimg.com/vi/bsXCxK99cKU/maxresdefault.jpg How to build observability into a ML Platform As machine learning becomes more prevalent across nearly every business and industry, making sure that these technologies are working and delivering quality is critical. In her talk, Alicia will discuss the importance of machine learning observability and why it should be a fundamental tool of modern machine learning architectures. Not only does it ensure models are accurate, but it helps teams iterate and improve models quicker. Alicia will dive into how Shopify has been prototyping building observability into different parts of its machine learning platform. This talk will provide insights on how to track model performance, how to catch any unexpected or erroneous behaviour, what types of behavior to look for in your data (e.g. drift, quality metrics) and in your model/predictions, and how observability could work with large language models and Chat AIs. https://www.youtube.com/watch?v=bsXCxK99cKU https://www.youtube-nocookie.com/embed/bsXCxK99cKU 2023-06-18 yes https://pycon.de/archive/2023/talks/how-to-connect-your-application-to-the-world-and-avoid-sleepless-nights/ 2023-06-18 https://pycon.de/static/media/social/talks/MTXCHH.png How to connect your application to the world (and avoid sleepless nights) https://i.ytimg.com/vi/nQtCkvFBcDs/maxresdefault.jpg How to connect your application to the world (and avoid sleepless nights) Let’s say you are the ruler of a remote island. For it to succeed and thrive you can’t expect it to be isolated from the world. You need to establish trade routes, offer your products to other islands, and import items from them. Doing this will certainly make your economy grow! We’re not going to talk about land masses or commerce, however, you should think of your application as an island that needs to connect to other applications to succeed. Unfortunately, the sea is treacherous and is not always very consistent, similar to the networks you use to connect your application to the world. We will explore some techniques and libraries in the Python ecosystem used to make your life easier while dealing with external services. From asynchronicity, caching, testing, and building abstractions on top of the APIs you consume, you will definitely learn some strategies to build your connected application gracefully, and avoid those pesky 2 AM errors that keep you awake. https://www.youtube.com/watch?v=nQtCkvFBcDs https://www.youtube-nocookie.com/embed/nQtCkvFBcDs 2023-06-18 yes https://pycon.de/archive/2023/talks/how-to-increase-diversity-in-open-source-communities/ 2023-06-18 https://pycon.de/static/media/social/talks/7SYVML.png How to increase diversity in open source communities https://i.ytimg.com/vi/6k5QiVPHihU/maxresdefault.jpg How to increase diversity in open source communities Today state of the art technology and scientific research strongly depend on open source libraries. The demographic of the contributors to these libraries is predominantly white and male [1][2][3][4]. This situation creates problems not only for individual contributors outside of this demographic but also for open source projects such as loss of career opportunities and less robust technologies, respectively [1][7]. In recent years there have been a number of various recommendations and initiatives to increase the participation in open source projects of groups who are underrepresented in this domain [1][3][5][6]. While these efforts are valuable and much needed, contributor diversity remains a challenge in open source communities [2][3][7]. This talk highlights the underlying problems and explores how we can overcome them. https://www.youtube.com/watch?v=6k5QiVPHihU https://www.youtube-nocookie.com/embed/6k5QiVPHihU 2023-06-18 yes https://pycon.de/archive/2023/talks/how-to-teach-nlp-to-a-newbie-get-them-started-on-their-first-project/ 2023-06-18 https://pycon.de/static/media/social/talks/BCRDQ8.png How to teach NLP to a newbie & get them started on their first project https://i.ytimg.com/vi/M036ltfct_8/maxresdefault.jpg How to teach NLP to a newbie & get them started on their first project The materials presented during this tutorial are open source and can be used by coaches and tutors who want to teach their students how to use Python for text processing and text classification. (A minimal understanding of programming (in any language) is required by the students) https://www.youtube.com/watch?v=M036ltfct_8 https://www.youtube-nocookie.com/embed/M036ltfct_8 2023-06-18 yes https://pycon.de/archive/2023/talks/hyperparameter-optimization-for-the-impatient/ 2023-06-18 https://pycon.de/static/media/social/talks/DECAHT.png Hyperparameter optimization for the impatient https://i.ytimg.com/vi/onX6fXzp9Yk/maxresdefault.jpg Hyperparameter optimization for the impatient In the last years, Hyperparameter Optimization (HPO) became a fundamental step in the training of Machine Learning (ML) models and in the creation of automatic ML pipelines. Unfortunately, while HPO improves the predictive performance of the final model, it comes with a significant cost both in terms of computational resources and waiting time. This leads many practitioners to try to lower the cost of HPO by employing unreliable heuristics. In this talk we will provide simple and practical algorithms for users that want to train models with almost-optimal predictive performance, while incurring in a significantly lower cost and waiting time. The presented algorithms are agnostic to the application and the model being trained so they can be useful in a wide range of scenarios. We provide results from an extensive experimental activity on public benchmarks, including comparisons with well-known techniques like Bayesian Optimization (BO), ASHA, Successive Halving. We will describe in which scenarios the biggest gains are observed (up to 30x) and provide examples for how to use these algorithms in a real-world environment. All the code used for this talk is available on (GitHub)[https://github.com/awslabs/syne-tune]. https://www.youtube.com/watch?v=onX6fXzp9Yk https://www.youtube-nocookie.com/embed/onX6fXzp9Yk 2023-06-18 yes https://pycon.de/archive/2023/talks/improving-machine-learning-from-human-feedback/ 2023-06-18 https://pycon.de/static/media/social/talks/AUJYP7.png Improving Machine Learning from Human Feedback https://i.ytimg.com/vi/IbuNQsM9uAA/maxresdefault.jpg Improving Machine Learning from Human Feedback Large generative models rely upon massive data sets that are collected automatically. For example, GPT-3 was trained with data from “Common Crawl” and “Web Text”, among other sources. As the saying goes — bigger isn’t always better. While powerful, these data sets (and the models that they create) often come at a cost, bringing their “internet-scale biases” along with their “internet-trained models.” While powerful, these models beg the question — is unsupervised learning the best future for machine learning? ML researchers have developed new model-tuning techniques to address the known biases within existing models and improve their performance (as measured by response preference, truthfulness, toxicity, and result generalization). All of this at a fraction of the initial training cost. In this talk, we will explore these techniques, known as Reinforcement Learning from Human Feedback (RLHF), and how open-source machine learning tools like PyTorch and Label Studio can be used to tune off-the-shelf models using direct human feedback. https://www.youtube.com/watch?v=IbuNQsM9uAA https://www.youtube-nocookie.com/embed/IbuNQsM9uAA 2023-06-18 yes https://pycon.de/archive/2023/talks/incorporating-gpt-3-into-practical-nlp-workflows/ 2023-06-18 https://pycon.de/static/media/social/talks/77MWVW.png Incorporating GPT-3 into practical NLP workflows https://i.ytimg.com/vi/Bd2ciwinFUE/maxresdefault.jpg Incorporating GPT-3 into practical NLP workflows In this talk, I'll show how large language models such as GPT-3 complement rather than replace existing machine learning workflows. Initial annotations are gathered from the OpenAI API via zero- or few-shot learning, and then corrected by a human decision maker using an annotation tool. The resulting annotations can then be used to train and evaluate models as normal. This process results in higher accuracy than can be achieved from the OpenAI API alone, with the added benefit that you'll own and control the model for runtime. https://www.youtube.com/watch?v=Bd2ciwinFUE https://www.youtube-nocookie.com/embed/Bd2ciwinFUE 2023-06-18 yes https://pycon.de/archive/2023/talks/introducing-fastkafka/ 2023-06-18 https://pycon.de/static/media/social/talks/HLMGHX.png Introducing FastKafka https://i.ytimg.com/vi/M18bhWVizjI/maxresdefault.jpg Introducing FastKafka FastKafka is a Python library that makes it easy to connect to Apache Kafka queues and send and receive messages. In this talk, we will introduce the library and its features for working with Kafka queues in Python. We will discuss the motivations for creating the library, how it compares to other Kafka client libraries, and how to use its decorators to define functions for consuming and producing messages. We will also demonstrate how to use these functions to build a simple application that sends and receives messages from the queue. This talk will be of interest to Python developers looking for an easy-to-use solution for working with Kafka. The documentation of the library can be found here: https://fastkafka.airt.ai/ https://www.youtube.com/watch?v=M18bhWVizjI https://www.youtube-nocookie.com/embed/M18bhWVizjI 2023-06-18 yes https://pycon.de/archive/2023/talks/introduction-to-async-programming/ 2023-06-18 https://pycon.de/static/media/social/talks/PPXA79.png Introduction to Async programming https://i.ytimg.com/vi/osGGX3tcwkc/maxresdefault.jpg Introduction to Async programming Asynchronous programming is a type of parallel programming in which a unit of work is allowed to run separately from the primary application thread. Post execution, it notifies the main thread about the completion or failure of the worker thread. There are numerous benefits to using it, such as improved application performance, enhanced responsiveness, and effective usage of CPU. Asynchronicity seems to be a big reason why Node.js is so popular for server-side programming. Most of the code we write, especially in heavy IO applications like websites, depends on external resources. This could be anything from a remote database POST API call. As soon as you ask for any of these resources, your code is waiting around for process completion with nothing to do. With asynchronous programming, you allow your code to handle other tasks while waiting for these other resources to respond. In this session, we are going to talk about asynchronous programming in Python. Its benefits and multiple ways to implement it. https://www.youtube.com/watch?v=osGGX3tcwkc https://www.youtube-nocookie.com/embed/osGGX3tcwkc 2023-06-18 yes https://pycon.de/archive/2023/talks/keynote-a-journey-through-4-industries-with-python-python-s-versatile-problem-solving-toolkit/ 2023-02-23 https://pycon.de/static/media/social/talks/NMMT8M.png Keynote - A journey through 4 industries with Python: Python's versatile problem-solving toolkit https://pycon.de/archive/2023/talks/keynote-how-are-we-managing-data-teams-management-irl/ 2023-02-28 https://pycon.de/static/media/social/talks/MBH7GB.png Keynote - How Are We Managing? Data Teams Management IRL https://pycon.de/archive/2023/talks/keynote-lorem-ipsum-dolor-sit-amet/ 2023-02-23 https://pycon.de/static/media/social/talks/HJ9J7Z.png Keynote - Lorem ipsum dolor sit amet https://pycon.de/archive/2023/talks/keynote-towards-learned-database-systems/ 2023-02-23 https://pycon.de/static/media/social/talks/JZSYA3.png Keynote - Towards Learned Database Systems https://pycon.de/archive/2023/talks/large-scale-feature-engineering-and-datascience-with-python-snowflake/ 2023-06-18 https://pycon.de/static/media/social/talks/3TYND7.png Large Scale Feature Engineering and Datascience with Python & Snowflake https://i.ytimg.com/vi/mpY7auHK3zw/maxresdefault.jpg Large Scale Feature Engineering and Datascience with Python & Snowflake [Snowflake](https://www.snowflake.com/en/) as a data platform is the core data repository of many large organizations. With the introduction of Snowflake's [Snowpark for Python](https://github.com/snowflakedb/snowpark-python), Python developers can now collaborate and build on one platform with a secure Python sandbox, providing developers with dynamic scalability & elasticity as well as security and compliance. In this talk I'll explain the core concepts of Snowpark for Python and how they can be used for large scale feature engineering and data science. https://www.youtube.com/watch?v=mpY7auHK3zw https://www.youtube-nocookie.com/embed/mpY7auHK3zw 2023-06-18 yes https://pycon.de/archive/2023/talks/let-s-contribute-to-pandas-3-hours-1/ 2022-12-22 https://pycon.de/static/media/social/talks/KUKU9Z.png Let's contribute to pandas (3 hours) #1 https://pycon.de/archive/2023/talks/let-s-contribute-to-pandas-3-hours-2/ 2023-02-22 https://pycon.de/static/media/social/talks/YWTRSG.png Let's contribute to pandas (3 hours) #2 https://pycon.de/archive/2023/talks/machine-learning-lifecycle-for-nlp-classification-in-e-commerce/ 2023-06-18 https://pycon.de/static/media/social/talks/8VYHKG.png Machine Learning Lifecycle for NLP Classification in E-Commerce https://i.ytimg.com/vi/4mGvH3ms6B8/maxresdefault.jpg Machine Learning Lifecycle for NLP Classification in E-Commerce Running machine learning models in a production environment brings its own challenges. In this talk we would like to present our solution of a machine learning lifecycle for the text-based cataloging classification system from idealo.de. We will share lessons learned and talk about our experiences during the lifecycle migration from a hosted cluster to a cloud solution within the last 3 years. In addition, we will outline how we embedded our ML components as part of the overall idealo.de processing architecture. https://www.youtube.com/watch?v=4mGvH3ms6B8 https://www.youtube-nocookie.com/embed/4mGvH3ms6B8 2023-06-18 yes https://pycon.de/archive/2023/talks/maps-with-django/ 2023-06-18 https://pycon.de/static/media/social/talks/KYLLZA.png Maps with Django https://i.ytimg.com/vi/0L3WD9KgKpM/maxresdefault.jpg Maps with Django Keeping in mind the **Pythonic** principle that _“simple is better than complex”_ we'll see how to create a web **map** with the **Python** based _web framework_ **Django** using its **GeoDjango** module, storing _geographic data_ in your _local database_ on which to run _geospatial queries_. https://www.youtube.com/watch?v=0L3WD9KgKpM https://www.youtube-nocookie.com/embed/0L3WD9KgKpM 2023-06-18 yes https://pycon.de/archive/2023/talks/maximizing-efficiency-and-scalability-in-open-source-mlops-a-step-by-step-approach/ 2023-06-18 https://pycon.de/static/media/social/talks/CTKC7B.png Maximizing Efficiency and Scalability in Open-Source MLOps: A Step-by-Step Approach https://i.ytimg.com/vi/NFDxfeXgPGM/maxresdefault.jpg Maximizing Efficiency and Scalability in Open-Source MLOps: A Step-by-Step Approach This talk presents a novel approach to MLOps that combines the benefits of open-source technologies with the power and cost-effectiveness of cloud computing platforms. By using tools such as Terraform, MLflow, and Feast, we demonstrate how to build a scalable and maintainable ML system on the cloud that is accessible to ML Engineers and Data Scientists. Our approach leverages cloud managed services for the entire ML lifecycle, reducing the complexity and overhead of maintenance and eliminating the vendor lock-in and additional costs associated with managed MLOps SaaS services. This innovative approach to MLOps allows organizations to take full advantage of the potential of machine learning while minimizing cost and complexity. https://www.youtube.com/watch?v=NFDxfeXgPGM https://www.youtube-nocookie.com/embed/NFDxfeXgPGM 2023-06-18 yes https://pycon.de/archive/2023/talks/methods-for-text-style-transfer-text-detoxification-case/ 2023-06-18 https://pycon.de/static/media/social/talks/UECWHD.png Methods for Text Style Transfer: Text Detoxification Case https://i.ytimg.com/vi/8I5tZvcmIis/maxresdefault.jpg Methods for Text Style Transfer: Text Detoxification Case Global access to the Internet has enabled the spread of information throughout the world and has offered many new possibilities. On the other hand, alongside the advantages, the exponential and uncontrolled growth of user-generated content on the Internet has also facilitated the spread of toxicity and hate speech. Much work has been done in the direction of offensive speech detection. However, there is another more proactive way to fight toxic speech -- how a suggestion for a user as a detoxified version of the message. In this presentation, we will provide an overview how texts detoxification task can be solved. The proposed approaches can be reused for any text style transfer task for both monolingual and multilingual use-cases. https://www.youtube.com/watch?v=8I5tZvcmIis https://www.youtube-nocookie.com/embed/8I5tZvcmIis 2023-06-18 yes https://pycon.de/archive/2023/talks/mlops-in-practice-our-journey-from-batch-to-real-time-inference/ 2023-06-18 https://pycon.de/static/media/social/talks/QLCNN9.png MLOps in practice: our journey from batch to real-time inference https://i.ytimg.com/vi/gvBaADatX9Y/maxresdefault.jpg MLOps in practice: our journey from batch to real-time inference I will present the challenges we encountered while migrating an ML model from batch to real-time predictions and how we handled them. In particular, I will focus on the design decisions and open-source tools we built to test the code, data and models as part of the CI/CD pipeline and enable us to ship fast with confidence. https://www.youtube.com/watch?v=gvBaADatX9Y https://www.youtube-nocookie.com/embed/gvBaADatX9Y 2023-06-18 yes https://pycon.de/archive/2023/talks/modern-typed-python-dive-into-a-mature-ecosystem-from-web-dev-to-machine-learning/ 2023-06-18 https://pycon.de/static/media/social/talks/LCCGTT.png Modern typed python: dive into a mature ecosystem from web dev to machine learning https://i.ytimg.com/vi/6MaCfvgqfUU/maxresdefault.jpg Modern typed python: dive into a mature ecosystem from web dev to machine learning Typing is at the center of „modern Python“, and tools (mypy, beartype) and libraries (FastAPI, SQLModel, Pydantic, DocArray) based on it are slowly eating the Python world. This talks explores the benefits of Python type hints, and shows how they are infiltrating the next big domain: Machine Learning https://www.youtube.com/watch?v=6MaCfvgqfUU https://www.youtube-nocookie.com/embed/6MaCfvgqfUU 2023-06-18 yes https://pycon.de/archive/2023/talks/monorepos-with-python/ 2023-06-18 https://pycon.de/static/media/social/talks/XDRNQC.png Monorepos with Python https://i.ytimg.com/vi/7_G4H2tPupo/maxresdefault.jpg Monorepos with Python Working with python is fun. Managing python packaging, linters, tests, CI, etc. is not as fun. Every maintainer needs to worry about consistent styling, quality, speed of tests, etc as the project grows. Monorepos have been successful in other communities - how does it work in Python ? https://www.youtube.com/watch?v=7_G4H2tPupo https://www.youtube-nocookie.com/embed/7_G4H2tPupo 2023-06-18 yes https://pycon.de/archive/2023/talks/most-of-you-don-t-need-spark-large-scale-data-management-on-a-budget-with-python/ 2023-06-18 https://pycon.de/static/media/social/talks/V9HBUU.png Most of you don't need Spark. Large-scale data management on a budget with Python https://i.ytimg.com/vi/OsYcsv4VkO8/maxresdefault.jpg Most of you don't need Spark. Large-scale data management on a budget with Python The Python data ecosystem has matured during the last decade and there are less and less reasons to rely only large batch process executed in a Spark cluster, but with every large ecosystem, putting together the key pieces of technology takes some effort. There are now better storage technologies, streaming execution engines, query planners, and low level compute libraries. And modern hardware is way more powerful than what you'd probably expect. In this workshop we will explore some global-warming-reducing techniques to build more efficient data transformation pipelines in Python, and a little bit of Rust. https://www.youtube.com/watch?v=OsYcsv4VkO8 https://www.youtube-nocookie.com/embed/OsYcsv4VkO8 2023-06-18 yes https://pycon.de/archive/2023/talks/neo4j-graph-databases-for-climate-policy/ 2023-06-18 https://pycon.de/static/media/social/talks/YTHXML.png Neo4j graph databases for climate policy https://i.ytimg.com/vi/gJ8-aTsVFSA/maxresdefault.jpg Neo4j graph databases for climate policy In this talk we walkthrough our experience using Neo4j and Python to model climate policy as a graph database. We discuss how we did it, some of the challenges we faced, and what we learnt along the way! https://www.youtube.com/watch?v=gJ8-aTsVFSA https://www.youtube-nocookie.com/embed/gJ8-aTsVFSA 2023-06-18 yes https://pycon.de/archive/2023/talks/observability-for-distributed-computing-with-dask/ 2023-06-18 https://pycon.de/static/media/social/talks/SSTCTS.png Observability for Distributed Computing with Dask https://i.ytimg.com/vi/Qp90ka5ZMqg/maxresdefault.jpg Observability for Distributed Computing with Dask Debugging is hard. Distributed debugging is hell. Dask is a popular library for parallel and distributed computing in Python. Dask is commonly used in data science, actual science, data engineering, and machine learning to distribute workloads onto clusters of many hundreds of workers with ease. However, when things go wrong life can become difficult due to all of the moving parts. These parts include your code, other PyData libraries like NumPy/pandas, the machines you’re running on, the network between them, storage, the cloud, and of course issues with Dask itself. It can be difficult to understand what is going on, especially when things seem slower than they should be or fail unexpectedly. Observability is the key to sanity and success. In this talk, we describe the tools Dask offers to help you observe your distributed cluster, analyze performance, and monitor your cluster to react to unexpected changes quickly. We will dive into distributed logging, automated metrics, event-based monitoring, and root-causing problems with diagnostic tooling. Throughout the talk, we will leverage real-world use cases to show how these tools help to identify and solve problems for large-scale users in the wild. This talk should be particularly insightful for Dask users, but the approaches to observing distributed systems should be relevant to anyone operating at scale in production. https://www.youtube.com/watch?v=Qp90ka5ZMqg https://www.youtube-nocookie.com/embed/Qp90ka5ZMqg 2023-06-18 yes https://pycon.de/archive/2023/talks/pandas-2-0-and-beyond/ 2023-06-18 https://pycon.de/static/media/social/talks/DB3KC7.png Pandas 2.0 and beyond https://i.ytimg.com/vi/7QQZ1hrHG1s/maxresdefault.jpg Pandas 2.0 and beyond Pandas has reached a 2.0 milestone in 2023. But what does that mean? And what is coming after 2.0? This talk will give an overview of what happened in the latest releases of pandas and highlight some topics and major new features the pandas project is working on. https://www.youtube.com/watch?v=7QQZ1hrHG1s https://www.youtube-nocookie.com/embed/7QQZ1hrHG1s 2023-06-18 yes https://pycon.de/archive/2023/talks/performing-root-cause-analysis-with-dowhy-a-causal-machine-learning-library/ 2023-06-18 https://pycon.de/static/media/social/talks/FVWF7R.png Performing Root Cause Analysis with DoWhy, a Causal Machine-Learning Library https://i.ytimg.com/vi/icpHrbDlGaw/maxresdefault.jpg Performing Root Cause Analysis with DoWhy, a Causal Machine-Learning Library In this talk, we will introduce the audience to [DoWhy](https://www.pywhy.org/dowhy), a library for causal machine-learning (ML). We will introduce typical problems where causal ML can be applied and will specifically do a deep dive on root cause analysis using DoWhy. To do this, we will lay out what typical problem spaces for causal ML look like, what kind of problems we're trying to solve, and then show how to use DoWhy's API to solve these problems. Expect to see a lot of code with a hands-on example. We will close this session by zooming out a bit and also talk about the PyWhy organization governing DoWhy. https://www.youtube.com/watch?v=icpHrbDlGaw https://www.youtube-nocookie.com/embed/icpHrbDlGaw 2023-06-18 yes https://pycon.de/archive/2023/talks/polars-make-the-switch-to-lightning-fast-dataframes/ 2023-06-18 https://pycon.de/static/media/social/talks/S79HEH.png Polars - make the switch to lightning-fast dataframes https://i.ytimg.com/vi/CtkMzCIXOWk/maxresdefault.jpg Polars - make the switch to lightning-fast dataframes In this talk, we will report on our experiences switching from Pandas to Polars in a real-world ML project. Polars is a new high-performance dataframe library for Python based on Apache Arrow and written in Rust. We will compare the performance of polars with the popular pandas library, and show how polars can provide significant speed improvements for data manipulation and analysis tasks. We will also discuss the unique features of polars, such as its ability to handle large datasets that do not fit into memory, and how it feels in practice to make the switch from Pandas. This talk is aimed at data scientists, analysts, and anyone interested in fast and efficient data processing in Python. https://www.youtube.com/watch?v=CtkMzCIXOWk https://www.youtube-nocookie.com/embed/CtkMzCIXOWk 2023-06-18 yes https://pycon.de/archive/2023/talks/postmodern-architecture-the-python-powered-modern-data-stack/ 2023-06-18 https://pycon.de/static/media/social/talks/A7B8P8.png Postmodern Architecture: The Python Powered Modern Data Stack https://i.ytimg.com/vi/na7yqvz5-B4/maxresdefault.jpg Postmodern Architecture: The Python Powered Modern Data Stack The Modern Data Stack has brought a lot of new buzzwords into the data engineering lexicon: "data mesh", "data observability", "reverse ETL", "data lineage", "analytics engineering". In this light-hearted talk we will demystify the evolving revolution that will define the future of data analytics & engineering teams. Our journey begins with the PyData Stack: pandas pipelines powering ETL workflows...clean code, tested code, data validation, perfect for in-memory workflows. As demand for self-serve analytics grows, new data sources bring more APIs to model, more code to maintain, DAG workflow orchestration tools, new nuances to capture ("the tax team defines revenue differently"), more dashboards, more not-quite-bugs ("but my number says this..."). This data maturity journey is a well-trodden path with common pitfalls & opportunities. After dashboards comes predictive modelling ("what will happen"), prescriptive modelling ("what should we do?"), perhaps eventually automated decision making. Getting there is much easier with the advent of the Python Powered Modern Data Stack. In this talk, we will cover the shift from ETL to ELT, the open-source Modern Data Stack tools you should know, with a focus on how dbt's new Python integration is changing how data pipelines are built, run, tested & maintained. By understanding the latest trends & buzzwords, attendees will gain a deeper insight into Python's role at the core of the future of data engineering. https://www.youtube.com/watch?v=na7yqvz5-B4 https://www.youtube-nocookie.com/embed/na7yqvz5-B4 2023-06-18 yes https://pycon.de/archive/2023/talks/practical-session-learning-on-heterogeneous-graphs-with-pyg/ 2023-06-18 https://pycon.de/static/media/social/talks/PQZR3Q.png Practical Session: Learning on Heterogeneous Graphs with PyG https://i.ytimg.com/vi/ORh-3Nhz_mo/maxresdefault.jpg Practical Session: Learning on Heterogeneous Graphs with PyG Learn how to build and analyze heterogeneous graphs using PyG, a machine graph learning library in Python. This workshop will provide a practical introduction to the concept of heterogeneous graphs and their applications, including their ability to capture the complexity and diversity of real-world systems. Participants will gain experience in creating a heterogeneous graph from multiple data tables, preparing a dataset, and implementing and training a model using PyG. https://www.youtube.com/watch?v=ORh-3Nhz_mo https://www.youtube-nocookie.com/embed/ORh-3Nhz_mo 2023-06-18 yes https://pycon.de/archive/2023/talks/pragmatic-ways-of-using-rust-in-your-data-project/ 2023-06-18 https://pycon.de/static/media/social/talks/MQHTHY.png Pragmatic ways of using Rust in your data project https://i.ytimg.com/vi/Jk9NXfvgclU/maxresdefault.jpg Pragmatic ways of using Rust in your data project Writing efficient data pipelines in Python can be tricky. The standard recommendation is to use vectorized functions implemented in Numpy, Pandas, or the like. However, what to do, when the processing task does not fit these libraries? Using plain Python for processing can result in lacking performance, in particular when handling large data sets. Rust is a modern, performance-oriented programming language that is already widely used by the Python community. Augmenting data processing steps with Rust can result in substantial speed ups. In this talk will present strategies of using Rust in a larger Python data processing pipeline with a particular focus on pragmatism and minimizing integration efforts. https://www.youtube.com/watch?v=Jk9NXfvgclU https://www.youtube-nocookie.com/embed/Jk9NXfvgclU 2023-06-18 yes https://pycon.de/archive/2023/talks/prompt-engineering-101-beginner-intro-to-langchain-the-shovel-of-our-chatgpt-gold-rush/ 2023-04-13 https://pycon.de/static/media/social/talks/MSZG7B.png Prompt Engineering 101: Beginner intro to LangChain, the shovel of our ChatGPT gold rush." https://pycon.de/archive/2023/talks/pyladies-panel-session-tech-illusions-and-the-unbalanced-society-finding-solutions-for-a-better-future/ 2023-06-18 https://pycon.de/static/media/social/talks/HDUAM9.png PyLadies Panel Session. Tech Illusions and the Unbalanced Society: Finding Solutions for a Better Future https://i.ytimg.com/vi/C3Yd6vQaGaQ/maxresdefault.jpg PyLadies Panel Session. Tech Illusions and the Unbalanced Society: Finding Solutions for a Better Future During this panel, we’ll discuss the significant role PyLadies chapters around the world have played in advocating for gender representation and leadership and combating biases and the gender pay gap. https://www.youtube.com/watch?v=C3Yd6vQaGaQ https://www.youtube-nocookie.com/embed/C3Yd6vQaGaQ 2023-06-18 yes https://pycon.de/archive/2023/talks/pyladies-workshop/ 2023-06-18 https://pycon.de/static/media/social/talks/7SVZR3.png PyLadies Workshop https://i.ytimg.com/vi/bypVJM5zu3Q/maxresdefault.jpg PyLadies Workshop A workshop for PyLadies members with the Berlin Tech Workers Council discussing the legal frameworks on contracts and termination agreements, as well as how employees can defend themselves in situations where they are made redundant due to mass layoffs. https://www.youtube.com/watch?v=bypVJM5zu3Q https://www.youtube-nocookie.com/embed/bypVJM5zu3Q 2023-06-18 yes https://pycon.de/archive/2023/talks/raised-by-pandas-striving-for-more-an-opinionated-introduction-to-polars/ 2023-06-18 https://pycon.de/static/media/social/talks/Z8PESY.png Raised by Pandas, striving for more: An opinionated introduction to Polars https://i.ytimg.com/vi/7xcUvzERwx0/maxresdefault.jpg Raised by Pandas, striving for more: An opinionated introduction to Polars Pandas is the de-facto standard for data manipulation in python, which I personally love for its flexible syntax and interoperability. But Pandas has well-known drawbacks such as memory in-efficiency, inconsistent missing data handling and lacking multicore-support. Multiple open-source projects aim to solve those issues, the most interesting is Polars. Polars uses Rust and Apache Arrow to win in all kinds of performance-benchmarks and evolves fast. But is it already stable enough to migrate an existing Pandas' codebase? And does it meet the high-expectations on query language flexibility of long-time Pandas-lovers? In this talk, I will explain, how Polars can be that fast, and present my insights on where Polars shines and in which scenarios I stay with pandas (at least for now!) https://www.youtube.com/watch?v=7xcUvzERwx0 https://www.youtube-nocookie.com/embed/7xcUvzERwx0 2023-06-18 yes https://pycon.de/archive/2023/talks/rethinking-codes-of-conduct/ 2023-06-27 https://pycon.de/static/media/social/talks/AWBLKN.png Rethinking codes of conduct https://i.ytimg.com/vi/0OXP0UegwCM/maxresdefault.jpg Rethinking codes of conduct Did you know that the Python Software Foundation Code of Conduct is turning 10 years old in 2023? It was voted in as they felt they were “unbalanced and not seeing the true spectrum of the greater community”. Why is that a big thing? Come to my talk and find out! https://www.youtube.com/watch?v=0OXP0UegwCM https://www.youtube-nocookie.com/embed/0OXP0UegwCM 2023-06-27 yes https://pycon.de/archive/2023/talks/rusty-python-a-case-study/ 2023-06-18 https://pycon.de/static/media/social/talks/LMGF8V.png Rusty Python: A Case Study https://i.ytimg.com/vi/Y5XQR0wUEyM/maxresdefault.jpg Rusty Python: A Case Study Python is a very expressive and powerful language, but it is not always the fastest option for performance-critical parts of an application. Rust, on the other hand, is known for its lightning-fast runtime and low-level control, making it an attractive option for speeding up performance-sensitive portions of Python programs. In this talk, we will present a case study of using Rust to speed up a critical component of a Python application. We will cover the following topics: * An overview of Rust and its benefits for Python developers * Profiling and identifying performance bottlenecks in Python application * Implementing a solution in Rust and integrating it with the Python application using PyO3 * Measuring the performance improvements and comparing them to other optimization techniques Attendees will learn about the potential for using Rust to boost the performance of their Python programs and how to go about doing so in their own projects. https://www.youtube.com/watch?v=Y5XQR0wUEyM https://www.youtube-nocookie.com/embed/Y5XQR0wUEyM 2023-06-18 yes https://pycon.de/archive/2023/talks/shrinking-gigabyte-sized-scikit-learn-models-for-deployment/ 2023-06-18 https://pycon.de/static/media/social/talks/7EUPC3.png Shrinking gigabyte sized scikit-learn models for deployment https://i.ytimg.com/vi/dtUn1ifDBXQ/maxresdefault.jpg Shrinking gigabyte sized scikit-learn models for deployment We present an open source library to shrink pickled scikit-learn and lightgbm models. We will provide insights of how pickling ML models work and how to improve the disk representation. With this approach, we can reduce the deployment size of machine learning applications up to 6x. https://www.youtube.com/watch?v=dtUn1ifDBXQ https://www.youtube-nocookie.com/embed/dtUn1ifDBXQ 2023-06-18 yes https://pycon.de/archive/2023/talks/software-design-pattern-for-data-science/ 2023-06-18 https://pycon.de/static/media/social/talks/CBGJNY.png Software Design Pattern for Data Science https://i.ytimg.com/vi/RcNhY3vMcpY/maxresdefault.jpg Software Design Pattern for Data Science Even if every data science work is special, a lot can be learned from similar problems solved in the past. In this talk, I will share some specific software design concepts that data scientists can use to build better data products. https://www.youtube.com/watch?v=RcNhY3vMcpY https://www.youtube-nocookie.com/embed/RcNhY3vMcpY 2023-06-18 yes https://pycon.de/archive/2023/talks/specifying-behavior-with-protocols-typeclasses-or-traits-who-wears-it-better-python-scala-3-rust/ 2023-01-05 https://pycon.de/static/media/social/talks/MJRFLC.png Specifying behavior with Protocols, Typeclasses or Traits. Who wears it better (Python, Scala 3, Rust)? https://pycon.de/archive/2023/talks/staying-alert-how-to-implement-continuous-testing-for-machine-learning-models/ 2023-06-18 https://pycon.de/static/media/social/talks/CHLT3D.png Staying Alert: How to Implement Continuous Testing for Machine Learning Models https://i.ytimg.com/vi/4BgmBtsS0iA/maxresdefault.jpg Staying Alert: How to Implement Continuous Testing for Machine Learning Models Proper monitoring of machine learning models in production is essential to avoid performance issues. Setting up monitoring can be easy for a single model, but it often becomes challenging at scale or when you face alert fatigue based on many metrics and dashboards. In this talk, I will introduce the concept of test-based ML monitoring. I will explore how to prioritize metrics based on risks and model use cases, integrate checks in the prediction pipeline and standardize them across similar models and model lifecycle. I will also take an in-depth look at batch model monitoring architecture and the use of open-source tools for setup and analysis. https://www.youtube.com/watch?v=4BgmBtsS0iA https://www.youtube-nocookie.com/embed/4BgmBtsS0iA 2023-06-18 yes https://pycon.de/archive/2023/talks/streamlit-meets-webassembly-stlite/ 2023-06-18 https://pycon.de/static/media/social/talks/GYEZVW.png Streamlit meets WebAssembly - stlite https://i.ytimg.com/vi/XivJYZUm1GY/maxresdefault.jpg Streamlit meets WebAssembly - stlite Streamlit, a pure-Python data app framework, has been ported to Wasm as "stlite". See its power and convenience with many live examples and explore its internals from a technical perspective. You will learn to quickly create interactive in-browser apps using only Python. https://www.youtube.com/watch?v=XivJYZUm1GY https://www.youtube-nocookie.com/embed/XivJYZUm1GY 2023-06-18 yes https://pycon.de/archive/2023/talks/teaching-neural-networks-a-sense-of-geometry/ 2023-06-18 https://pycon.de/static/media/social/talks/3TH9UC.png Teaching Neural Networks a Sense of Geometry https://i.ytimg.com/vi/z1yHJyZZ8yA/maxresdefault.jpg Teaching Neural Networks a Sense of Geometry By taking neural networks back to the school bench and teaching them some elements of geometry and topology we can build algorithms that can reason about the shape of data. Surprisingly these methods can be useful not only for computer vision – to model input data such as images or point clouds through global, robust properties – but in a wide range of applications, such as evaluating and improving the learning of embeddings, or the distribution of samples originating from generative models. This is the promise of the emerging field of Topological Data Analysis (TDA) which we will introduce and review recent works at its intersection with machine learning. TDA can be seen as being part of the increasingly popular movement of Geometric Deep Learning which encourages us to go beyond seeing data only as vectors in Euclidean spaces and instead consider machine learning algorithms that encode other geometric priors. In the past couple of years TDA has started to take a step out of the academic bubble, to a large extent thanks to powerful Python libraries written as extensions to scikit-learn or PyTorch. https://www.youtube.com/watch?v=z1yHJyZZ8yA https://www.youtube-nocookie.com/embed/z1yHJyZZ8yA 2023-06-18 yes https://pycon.de/archive/2023/talks/the-battle-of-giants-causality-vs-nlp-from-theory-to-practice/ 2023-06-18 https://pycon.de/static/media/social/talks/GLQH8X.png The Battle of Giants: Causality vs NLP => From Theory to Practice https://i.ytimg.com/vi/Bd1XtGZhnmw/maxresdefault.jpg The Battle of Giants: Causality vs NLP => From Theory to Practice With an average of 3.2 new papers published on Arxiv every day in 2022, causal inference has exploded in popularity, attracting large amount of talent and interest from top researchers and institutions including industry giants like Amazon or Microsoft. Text data, with its high complexity, posits an exciting challenge for causal inference community. In the workshop, we'll review the latest advances in the field of Causal NLP and implement a causal Transformer model to demonstrate how to translate these developments into a practical solution that can bring real business value. All in Python! https://www.youtube.com/watch?v=Bd1XtGZhnmw https://www.youtube-nocookie.com/embed/Bd1XtGZhnmw 2023-06-18 yes https://pycon.de/archive/2023/talks/the-beauty-of-zarr/ 2023-06-18 https://pycon.de/static/media/social/talks/JY3R3Z.png The Beauty of Zarr https://i.ytimg.com/vi/OYaMi9WnQpA/maxresdefault.jpg The Beauty of Zarr In this talk, I’d be talking about [Zarr](https://zarr.dev/), an open-source data format for storing chunked, compressed N-dimensional arrays. This talk presents a systematic approach to understanding and implementing Zarr by showing how it works, the need for using it, and a hands-on session at the end. Zarr is based on an open [technical specification](https://zarr.readthedocs.io/en/stable/spec/v2.html), making implementations across several languages possible. I’d mainly talk about [Zarr’s Python](https://github.com/zarr-developers/zarr-python) implementation and show how it beautifully interoperates with the existing libraries in the PyData stack. https://www.youtube.com/watch?v=OYaMi9WnQpA https://www.youtube-nocookie.com/embed/OYaMi9WnQpA 2023-06-18 yes https://pycon.de/archive/2023/talks/the-bumps-in-the-road-a-retrospective-on-my-data-visualisation-mistakes/ 2023-06-18 https://pycon.de/static/media/social/talks/7FTL7H.png The bumps in the road: A retrospective on my data visualisation mistakes https://i.ytimg.com/vi/slOm7ztgnfM/maxresdefault.jpg The bumps in the road: A retrospective on my data visualisation mistakes We will delve into the importance of effective data visualisation in today's world. We will explore how it can help convey insights from data using Matplotlib and best practices for creating informative visualisations. We will also discuss the limitations of static visualisations and examine the role of continuous integration in streamlining the process and avoiding common pitfalls. By the end of this talk, you will have gained valuable insights and techniques for creating informative and accurate data visualisations, no matter what tools you're using. https://www.youtube.com/watch?v=slOm7ztgnfM https://www.youtube-nocookie.com/embed/slOm7ztgnfM 2023-06-18 yes https://pycon.de/archive/2023/talks/the-cpu-in-your-browser-webassembly-demystified/ 2023-06-18 https://pycon.de/static/media/social/talks/E77G9H.png The CPU in your browser: WebAssembly demystified https://i.ytimg.com/vi/VCkcv0ppYXs/maxresdefault.jpg The CPU in your browser: WebAssembly demystified In the recent years we saw an explosion of usage of Python in the browser: Pyodide, CPython on WASM, PyScript, etc. All of this is possible thanks to the powerful functionalities of the underlying platform, WebAssembly, which is essentially a virtual CPU inside the browser. https://www.youtube.com/watch?v=VCkcv0ppYXs https://www.youtube-nocookie.com/embed/VCkcv0ppYXs 2023-06-18 yes https://pycon.de/archive/2023/talks/the-future-of-the-jupyter-notebook-interface/ 2023-06-18 https://pycon.de/static/media/social/talks/VXPFFP.png The future of the Jupyter Notebook interface https://i.ytimg.com/vi/ajod3jrepIk/maxresdefault.jpg The future of the Jupyter Notebook interface Jupyter Notebooks have been a widely popular tool for data science in recent years due to their ability to combine code, text, and visualizations in a single document. Despite its popularity, the core functionality and user experience of the Classic Jupyter Notebook interface has remained largely unchanged over the past years. Lately the Jupyter Notebook project decided to base its next major version 7 on JupyterLab components and extensions, which means many JupyterLab features are also available to Jupyter Notebook users. In this presentation, we will demo the new features coming in Jupyter Notebook version 7 and how they are relevant to existing users of the Classic Notebook. https://www.youtube.com/watch?v=ajod3jrepIk https://www.youtube-nocookie.com/embed/ajod3jrepIk 2023-06-18 yes https://pycon.de/archive/2023/talks/the-spark-of-big-data-an-introduction-to-apache-spark/ 2023-06-18 https://pycon.de/static/media/social/talks/Q7GS8Y.png The Spark of Big Data: An Introduction to Apache Spark https://i.ytimg.com/vi/jOJceajwMGs/maxresdefault.jpg The Spark of Big Data: An Introduction to Apache Spark Get ready to level up your big data processing skills! Join us for an introductory talk on Apache Spark, the distributed computing system used by tech giants like Netflix and Amazon. We'll cover PySpark DataFrames and how to use them. Whether you're a Python developer new to big data or looking to explore new technologies, this talk is for you. You'll gain foundational knowledge about Apache Spark and its capabilities, and learn how to leverage DataFrames and SQL APIs to efficiently process large amounts of data. Don't miss out on this opportunity to up your big data game! https://www.youtube.com/watch?v=jOJceajwMGs https://www.youtube-nocookie.com/embed/jOJceajwMGs 2023-06-18 yes https://pycon.de/archive/2023/talks/the-state-of-production-machine-learning-in-2023/ 2023-06-18 https://pycon.de/static/media/social/talks/MLAGKM.png The State of Production Machine Learning in 2023 https://i.ytimg.com/vi/xRFX5taXNcA/maxresdefault.jpg The State of Production Machine Learning in 2023 As the number of production machine learning use-cases increase, we find ourselves facing new and bigger challenges where more is at stake. Because of this, it's critical to identify the key areas to focus our efforts, so we can ensure our machine learning pipelines are reliable and scalable. In this talk we dive into the state of production machine learning in the Python Ecosystem, and we will cover the concepts that make production machine learning so challenging, as well as some of the recommended tools available to tackle these challenges. This talk will cover key principles, patterns and frameworks around the open source frameworks powering single or multiple phases of the end-to-end ML lifecycle, incluing model training, deploying, monitoring, etc. We will be covering a high level overview of the production ML ecosystem and dive into best practices that have been abstracted from production use-cases of machine learning operations at scale, as well as how to leverage tools to that will allow us to deploy, explain, secure, monitor and scale production machine learning systems. https://www.youtube.com/watch?v=xRFX5taXNcA https://www.youtube-nocookie.com/embed/xRFX5taXNcA 2023-06-18 yes https://pycon.de/archive/2023/talks/thou-shall-judge-but-with-fairness-methods-to-ensure-an-unbiased-model/ 2023-06-18 https://pycon.de/static/media/social/talks/CBHYXG.png Thou Shall Judge But With Fairness: Methods to Ensure an Unbiased Model https://i.ytimg.com/vi/XVyohs7jejg/maxresdefault.jpg Thou Shall Judge But With Fairness: Methods to Ensure an Unbiased Model Is your model prejudicial? Is your model deviating from the predictions it ought to have made? Has your model misunderstood the concept? In the world of artificial intelligence and machine learning, the word "fairness" is particularly common. It is described as having the quality of being impartial or fair. Fairness in ML is essential for contemporary businesses. It helps build consumer confidence and demonstrates to customers that their issues are important. Additionally, it aids in ensuring adherence to guidelines established by authorities. So guaranteeing that the idea of responsible AI is upheld. In this talk, let's explore how certain sensitive features are influencing the model and introducing bias into it. We'll also look at how we can make it better. https://www.youtube.com/watch?v=XVyohs7jejg https://www.youtube-nocookie.com/embed/XVyohs7jejg 2023-06-18 yes https://pycon.de/archive/2023/talks/unlocking-information-creating-synthetic-data-for-open-access/ 2023-06-18 https://pycon.de/static/media/social/talks/J9KRKZ.png Unlocking Information - Creating Synthetic Data for Open Access. https://i.ytimg.com/vi/N1i_Z-WKaRs/maxresdefault.jpg Unlocking Information - Creating Synthetic Data for Open Access. Many good project ideas fail before they even start due to the sensitive personal data required. The good news: a synthetic version of this data does not need protection. Synthetic data copies the actual data's structure and statistical properties without recreating personally identifiable information. The bad news: It is difficult to create synthetic data for open-access use, without recreating the exact copy of actual data. This talk will give hands-on insights into synthetic data creation and challenges along its lifecycle. We will learn how to create and evaluate synthetic data for any use case using the open-source package Synthetic Data Vault. We will find answers to why it takes so long to synthesize the huge amount of data dormant in public administration. The talk addresses owners who want to create access to their private data as well as analysts looking to use synthetic data. After this session, listeners will know which steps to take to generate synthetic data for multi-purpose use and its limitations for real-world analyses. https://www.youtube.com/watch?v=N1i_Z-WKaRs https://www.youtube-nocookie.com/embed/N1i_Z-WKaRs 2023-06-18 yes https://pycon.de/archive/2023/talks/use-spark-from-anywhere-a-spark-client-in-python-powered-by-spark-connect/ 2023-06-18 https://pycon.de/static/media/social/talks/UNA9AN.png Use Spark from anywhere: A Spark client in Python powered by Spark Connect https://i.ytimg.com/vi/PzgPcvFDD4I/maxresdefault.jpg Use Spark from anywhere: A Spark client in Python powered by Spark Connect Over the past decade, developers, researchers, and the community have successfully built tens of thousands of data applications using Spark. Since then, use cases and requirements of data applications have evolved: Today, every application, from web services that run in application servers, interactive environments such as notebooks and IDEs, to phones and edge devices such as smart home devices, want to leverage the power of data. However, Spark's driver architecture is monolithic, running client applications on top of a scheduler, optimizer and analyzer. This architecture makes it hard to address these new requirements: there is no built-in capability to remotely connect to a Spark cluster from languages other than SQL. Spark Connect introduces a decoupled client-server architecture for Apache Spark that allows remote connectivity to Spark clusters using the DataFrame API and unresolved logical plans as the protocol. The separation between client and server allows Spark and its open ecosystem to be leveraged from everywhere. It can be embedded in modern data applications, in IDEs, Notebooks and programming languages. This talk highlights how simple it is to connect to Spark using Spark Connect from any data applications or IDEs. We will do a deep dive into the architecture of Spark Connect and give an outlook of how the community can participate in the extension of Spark Connect for new programming languages and frameworks - to bring the power of Spark everywhere. https://www.youtube.com/watch?v=PzgPcvFDD4I https://www.youtube-nocookie.com/embed/PzgPcvFDD4I 2023-06-18 yes https://pycon.de/archive/2023/talks/using-transformers-a-drama-in-512-tokens/ 2023-06-18 https://pycon.de/static/media/social/talks/SJCEFG.png Using transformers – a drama in 512 tokens https://i.ytimg.com/vi/TigtGzXnwJ8/maxresdefault.jpg Using transformers – a drama in 512 tokens “Got an NLP problem nowadays? Use transformers! Just download a pretrained model from the hub!” - every blog article ever As if it’s that easy, because nearly all pretrained models have a very annoying limitation: they can only process short input sequences. Not every NLP practitioner happens to work on tweets, but instead many of us have to deal with longer input sequences. What started as a minor design choice for BERT, got cemented by the research community over the years and now turns out to be my biggest headache: the 512 tokens limit. In this talk, we’ll ask a lot of dumb questions and get an equal number of unsatisfying answers: 1. How much text actually fits into 512 tokens? Spoiler: not enough to solve my use case, and I bet a lot of your use cases, too. 2. I can feed a sequence of any length into an RNN, why do transformers even have a limit? We’ll look into the architecture in more detail to understand that. 3. Somebody smart must have thought about this sequence length issue before, or not? Prepare yourself for a rant about benchmarks in NLP research. 4. So what can we do to handle longer input sequences? Enjoy my collection of mediocre workarounds. https://www.youtube.com/watch?v=TigtGzXnwJ8 https://www.youtube-nocookie.com/embed/TigtGzXnwJ8 2023-06-18 yes https://pycon.de/archive/2023/talks/visualizing-your-computer-vision-data-is-not-a-luxury-it-s-a-necessity-without-it-your-models-are-blind-and-so-do-you/ 2023-01-05 https://pycon.de/static/media/social/talks/X89787.png Visualizing your computer vision data is not a luxury, it's a necessity: without it, your models are blind and so do you. https://pycon.de/archive/2023/talks/wald-a-modern-sustainable-analytics-stack/ 2023-06-18 https://pycon.de/static/media/social/talks/TP7ABB.png WALD: A Modern & Sustainable Analytics Stack https://i.ytimg.com/vi/7GfbA6_a09I/maxresdefault.jpg WALD: A Modern & Sustainable Analytics Stack The name **WALD**-stack stems from the four technologies it is composed of, i.e. a cloud-computing **W**arehouse like Snowflake or Google BigQuery, the open-source data integration engine **A**irbyte, the open-source full-stack BI platform **L**ightdash, and the open-source data transformation tool **D**BT. Using a Formula 1 Grand Prix dataset, I will give an overview of how these four tools complement each other perfectly for analytics tasks in an ELT approach. You will learn the specific uses of each tool as well as their particular features. My talk is based on a full tutorial, which you can find under [waldstack.org](https://waldstack.org). https://www.youtube.com/watch?v=7GfbA6_a09I https://www.youtube-nocookie.com/embed/7GfbA6_a09I 2023-06-18 yes https://pycon.de/archive/2023/talks/what-are-you-yield-from/ 2023-06-18 https://pycon.de/static/media/social/talks/ENQBPJ.png What are you yield from? https://i.ytimg.com/vi/vO9U-2_q6K8/maxresdefault.jpg What are you yield from? Many developers avoid using generators. For example, many well-known python libraries use lists instead of generators. The generators themselves are slower than normal list loops, but their use in code greatly increases the speed of the application. Let’s discover why. https://www.youtube.com/watch?v=vO9U-2_q6K8 https://www.youtube-nocookie.com/embed/vO9U-2_q6K8 2023-06-18 yes https://pycon.de/archive/2023/talks/what-could-possibly-go-wrong-an-incomplete-guide-on-how-to-prevent-detect-mitigate-biases-in-data-products/ 2023-01-03 https://pycon.de/static/media/social/talks/NUF87W.png What could possibly go wrong? - An incomplete guide on how to prevent, detect & mitigate biases in data products https://pycon.de/archive/2023/talks/when-a-b-testing-isnt-an-option-an-introduction-to-quasi-experimental-methods/ 2023-06-18 https://pycon.de/static/media/social/talks/B8FKHC.png When A/B testing isn’t an option: an introduction to quasi-experimental methods https://i.ytimg.com/vi/2g61fv0rabI/maxresdefault.jpg When A/B testing isn’t an option: an introduction to quasi-experimental methods Identification of causal relationships through running experiments is not always possible. In this talk, an alternative approach towards it, quasi-experimental frameworks, is discussed. Additionally, I will present how to adjust well-known machine-learning algorithms so they can be used to quantify causal relationships. https://www.youtube.com/watch?v=2g61fv0rabI https://www.youtube-nocookie.com/embed/2g61fv0rabI 2023-06-18 yes https://pycon.de/archive/2023/talks/who-is-an-nlp-expert-lessons-learned-from-building-an-in-house-qa-system/ 2023-01-04 https://pycon.de/static/media/social/talks/U7WAQW.png “Who is an NLP expert?” - Lessons Learned from building an in-house QA-system https://pycon.de/archive/2023/talks/why-gpu-clusters-don-t-need-to-go-brrr-leverage-compound-sparsity-to-achieve-the-fastest-inference-performance-on-cpus/ 2023-01-05 https://pycon.de/static/media/social/talks/7NW7JC.png Why GPU Clusters Don't Need to Go Brrr? Leverage Compound Sparsity to Achieve the Fastest Inference Performance on CPUs https://pycon.de/archive/2023/talks/workshop-on-privilege-and-ethics-in-data/ 2023-01-04 https://pycon.de/static/media/social/talks/ZYB38R.png Workshop on Privilege and Ethics in Data https://pycon.de/archive/2023/talks/writing-plugin-friendly-python-applications/ 2023-06-18 https://pycon.de/static/media/social/talks/RDQH3W.png Writing Plugin Friendly Python Applications https://i.ytimg.com/vi/d40tBcqopAI/maxresdefault.jpg Writing Plugin Friendly Python Applications In modern software engineering, plugin systems are a ubiquitous way to extend and modify the behavior of applications and libraries. When software is written in a way that is plugin friendly, it encourages the use of modular organization where the contracts between the core software and the plugin have been well thought out. In this talk, we cover exactly how to define this contract and how you can start designing your software to be more plugin friendly. Throughout the talk we will be creating our own plugin friendly application using the [pluggy](https://pluggy.readthedocs.io/en/stable/) library to show these design principles in action. At the end of the talk, I also cover a real-life case study of how the package manager [conda](https://github.com/conda/conda) is currently making its 10 year old code more plugin friendly to illustrate how to retrofit an existing project. https://www.youtube.com/watch?v=d40tBcqopAI https://www.youtube-nocookie.com/embed/d40tBcqopAI 2023-06-18 yes https://pycon.de/archive/2023/talks/you-are-what-you-read-building-a-personal-internet-front-page-with-spacy-and-prodigy/ 2023-01-04 https://pycon.de/static/media/social/talks/NWSLUH.png You are what you read: Building a personal internet front-page with spaCy and Prodigy https://pycon.de/archive/2023/talks/you-ve-got-trust-issues-we-ve-got-solutions-differential-privacy/ 2022-12-17 https://pycon.de/static/media/social/talks/G9TATQ.png You've got trust issues, we've got solutions: Differential Privacy https://pycon.de/archive/2024/talks/525-days-working-full-time-on-foss-lessons-learned/ 2023-12-18 https://pycon.de/static/media/social/talks/ZMC9FU.png 525 days working full-time on FOSS: lessons learned https://pycon.de/archive/2024/talks/a-conceptual-and-practical-introduction-to-hilbert-space-gaussian-process-hsgp-approximation-methods/ 2024-06-23 https://pycon.de/static/media/social/talks/YWUZW9.png A conceptual and practical introduction to Hilbert Space Gaussian Process (HSGP) approximation methods https://i.ytimg.com/vi/SvefEqtoaxg/maxresdefault.jpg A conceptual and practical introduction to Hilbert Space Gaussian Process (HSGP) approximation methods In this talk, we explore a new method to approximate Gaussian processes using spectral analysis methods, known as the Hilbert Space Gaussian process (HSGP) approximation. This technique allows us to use and fit Gaussian processes at scale for concrete applications. We provide a basic introduction to the ideas behind the method and make them tangible by implementing them ourselves using Numpyro. We then present two concrete examples in practice using both Numpyro and PyMC. Namely time-varying coefficient regression and time series forecasting. https://www.youtube.com/watch?v=SvefEqtoaxg https://www.youtube-nocookie.com/embed/SvefEqtoaxg 2024-06-23 yes https://pycon.de/archive/2024/talks/a-deep-dive-into-the-arrow-columnar-format-with-pyarrow-and-nanoarrow/ 2024-06-23 https://pycon.de/static/media/social/talks/LERYUY.png A deep dive into the Arrow Columnar format with pyarrow and nanoarrow https://i.ytimg.com/vi/k0mkDXWfLb4/maxresdefault.jpg A deep dive into the Arrow Columnar format with pyarrow and nanoarrow Apache Arrow has become a de-facto standard for efficient in-memory columnar data representation. You might have heard about Arrow or using Arrow, but do you understand the format and why it’s so useful? This tutorial will dive deep into the details of the Arrow columnar format, the different types and buffer layouts, and explore those details interactively using the pyarrow and nanoarrow libraries. https://www.youtube.com/watch?v=k0mkDXWfLb4 https://www.youtube-nocookie.com/embed/k0mkDXWfLb4 2024-06-23 yes https://pycon.de/archive/2024/talks/a-retrieval-augmented-generation-system-to-query-the-scikit-learn-documentation/ 2024-06-23 https://pycon.de/static/media/social/talks/ZCKQVG.png A Retrieval Augmented Generation system to query the scikit-learn documentation https://i.ytimg.com/vi/a1F_qOn11xc/maxresdefault.jpg A Retrieval Augmented Generation system to query the scikit-learn documentation The scikit-learn website currently employs an "exact" search engine based on the Sphinx Python package, but it has limitations: it cannot handle spelling mistakes and queries based on natural language. To address these constraints, we experimented with using large language models (LLMs) and opted for a retrieval augmented generation (RAG) system due to resource constraints. This talk introduces our experimental RAG system for querying scikit-learn documentation. We focus on an open-source software stack and open-weight models. The talk presents the different stages of the RAG pipeline. We provide documentation scraping strategies that we designed based on numpydoc and sphinx-gallery, which are used to build vector indices for the lexical and semantic searches. We compare our RAG approach with an LLM-only approach to demonstrate the advantage of providing context. The source code for this experiment is available on GitHub: https://github.com/glemaitre/sklearn-ragger-duck. Finally, we discuss the gains and challenges of integrating such a system into an open-source project, including hosting and cost considerations, comparing it with alternative approaches. https://www.youtube.com/watch?v=a1F_qOn11xc https://www.youtube-nocookie.com/embed/a1F_qOn11xc 2024-06-23 yes https://pycon.de/archive/2024/talks/acknowledging-womens-contributions-in-the-python-community-through-podcast/ 2024-10-28 https://pycon.de/static/media/social/talks/BYH8Y8.png Acknowledging Women’s Contributions in the Python Community Through Podcast https://i.ytimg.com/vi/A4cNFup5fMc/maxresdefault.jpg Acknowledging Women’s Contributions in the Python Community Through Podcast The Python community has been making efforts in improving the diversity and representation among its members. There are examples of success stories such as PyCon US Charlas, PyLadies, Djangonaut, and Django Girls. Yet in the Python podcast community, women are still underrepresented, making up only 17% of invited guests among the popular podcast series. Being a guest in a podcast is a privilege, and an opportunity to influence the Python community. There are many women and underrepresented group members who have made impactful contributions to the Python community globally, and they deserve the recognition and to be heard by the rest of us. Disheartened by the lack of representation by women on Python podcasts, and inspired by others who have shown us how diversity in the community can be improved through intentionality, we decided to start a podcast with a goal to highlight their voices so that they could receive the recognition they deserve. In this talk, learn about them, and about our podcast series. We’ll also share how you can further help out cause in improving representation and diversity in the Python community. https://www.youtube.com/watch?v=A4cNFup5fMc https://www.youtube-nocookie.com/embed/A4cNFup5fMc 2024-10-28 yes https://pycon.de/archive/2024/talks/advanced-observability-with-opentelemetry-and-python/ 2024-10-28 https://pycon.de/static/media/social/talks/AQ8HUM.png Advanced Observability with OpenTelemetry and Python https://i.ytimg.com/vi/pAuxHdbWK3M/maxresdefault.jpg Advanced Observability with OpenTelemetry and Python As Python expands into serverless and cloud environments, popularizing distributed microservice architectures, we often face observability challenges that impact efficiency and complicate error tracing. This presentation introduces OpenTelemetry, an emerging industry standard that provides a framework for tracking the performance of not just our Python code, but also other system components like databases and message queues. Its API and SDK integrate seamlessly with Python, enabling a unified approach to gather, process, and export telemetry data from various sources within a distributed system. We will explore the setup and usage of OpenTelemetry's Python SDK through a practical scenario. The session will demonstrate how to convert an existing Flask microservice to use OpenTelemetry, using both automatic and manual instrumentation. Finally, we will examine how to utilize the exported data for enhanced system monitoring. https://www.youtube.com/watch?v=pAuxHdbWK3M https://www.youtube-nocookie.com/embed/pAuxHdbWK3M 2024-10-28 yes https://pycon.de/archive/2024/talks/analyzing-covid-19-protest-movements-a-multidimensional-approach-using-geo-social-media-data/ 2024-10-28 https://pycon.de/static/media/social/talks/CY97LS.png Analyzing COVID-19 Protest Movements: A Multidimensional Approach Using Geo-Social Media Data https://i.ytimg.com/vi/54OVK_ShXVY/maxresdefault.jpg Analyzing COVID-19 Protest Movements: A Multidimensional Approach Using Geo-Social Media Data The COVID-19 pandemic and associated policy measures lead to world-wide protest movements that were singled out by the spread of misinformation and conspiracy theories, predominantly on social media platforms. Publicly available social media data therefore is a powerful proxy for studying these protest movements. The data, consisting of user locations, follower relationships, and content information, allows to understand the geographical centers of activity, network structure, and key themes of conspiracy movements. This talk will present a multi-dimensional network analysis for the Austrian COVID-10 protest movement using Python libraries like geopandas, networkx and gensim. In particular, it will demonstrate how to identify geo-spatial hot spots using spatial statistics, densely connected clusters within the network by employing community detection techniques, as well as dominating content themes through topic modeling approaches. The presentation highlights how data-driven analysis enables further understanding of movements that may pose threats to democracy, alongside the importance of publicly available social media data for addressing societal challenges. https://www.youtube.com/watch?v=54OVK_ShXVY https://www.youtube-nocookie.com/embed/54OVK_ShXVY 2024-10-28 yes https://pycon.de/archive/2024/talks/async-awaits-mastering-asynchronous-python-in-fastapi/ 2024-10-28 https://pycon.de/static/media/social/talks/DPVJ7K.png Async Awaits: Mastering Asynchronous Python in FastAPI https://i.ytimg.com/vi/1z8LLSZSWHM/maxresdefault.jpg Async Awaits: Mastering Asynchronous Python in FastAPI In this talk, we delve into the transformative world of asynchronous programming in Python, tailored specifically for the FastAPI framework. This session will explore the fundamentals of async/await syntax, unveiling how it can optimize the performance and scalability of web applications. Attendees will gain practical insights into implementing asynchronous operations in FastAPI, from setting up to handling real-time data processing. This talk is perfect for Python developers eager to harness the power of asynchronous programming to build faster, more efficient web applications. Join us to unlock the full potential of Python's async capabilities within FastAPI's dynamic environment. https://www.youtube.com/watch?v=1z8LLSZSWHM https://www.youtube-nocookie.com/embed/1z8LLSZSWHM 2024-10-28 yes https://pycon.de/archive/2024/talks/asyncapp-my-contribution-to-hype-pythons-asyncio-a-bit-more/ 2024-10-28 https://pycon.de/static/media/social/talks/BA7FZL.png AsyncApp. My contribution to hype Pythons asyncio a bit more https://i.ytimg.com/vi/ya-JXEuYi1g/maxresdefault.jpg AsyncApp. My contribution to hype Pythons asyncio a bit more Asyncio use is now everywhere in the Python world, ... .. or is it? Being there since version 3.4 my impression is, that it is still not the go to solution when starting off new projects. It's not an obvious choice and traditional approaches still seem to be much preferred especially by beginners. So let me take you with me on a journey to create simple, yet powerful building blocks to build asyncio based applications using patterns that are easy to follow, lightweight and attractive. #asyncio #click #logging #psutil #redis #raspberrypi https://www.youtube.com/watch?v=ya-JXEuYi1g https://www.youtube-nocookie.com/embed/ya-JXEuYi1g 2024-10-28 yes https://pycon.de/archive/2024/talks/best-of-both-worlds-how-we-built-an-ai-aided-content-creation-tool-for-language-learning/ 2024-06-23 https://pycon.de/static/media/social/talks/Y3FLEH.png Best of both worlds - How we built an AI-aided content creation tool for language learning https://i.ytimg.com/vi/fIFA4eHBI8s/maxresdefault.jpg Best of both worlds - How we built an AI-aided content creation tool for language learning Discover how Babbel bridged the gap between tailored language learning and scalability through an AI-aided content creation tool. Our approach amalgamates human expertise with Generative Artificial Intelligence, enabling personalized content creation on a large scale. Join us on our development journey and the different iterations we went through. We will demo the tool's current version and its AI features. Learn about the tech stack and what lies ahead in our development pipeline. https://www.youtube.com/watch?v=fIFA4eHBI8s https://www.youtube-nocookie.com/embed/fIFA4eHBI8s 2024-06-23 yes https://pycon.de/archive/2024/talks/better-safe-than-sorry-threat-modeling-for-python-developers/ 2024-10-28 https://pycon.de/static/media/social/talks/PRH3QU.png Better safe than sorry: Threat Modeling for Python Developers https://i.ytimg.com/vi/Tq6kPnrZmrs/maxresdefault.jpg Better safe than sorry: Threat Modeling for Python Developers Every developer wants to write good code. Good code, that also means security against attackers and their threats. But how secure is your code really? The talk explains how you can use Threat Modeling to assess your application in a systematic approach against the threats that are relevant to your use cases and their attack surface. https://www.youtube.com/watch?v=Tq6kPnrZmrs https://www.youtube-nocookie.com/embed/Tq6kPnrZmrs 2024-10-28 yes https://pycon.de/archive/2024/talks/better-search-relevance-using-learning-to-rank-at-mobile-de/ 2024-06-23 https://pycon.de/static/media/social/talks/LMMM7D.png Better search relevance using Learning to Rank at mobile.de https://i.ytimg.com/vi/cwDnYWcMJ1c/maxresdefault.jpg Better search relevance using Learning to Rank at mobile.de At mobile.de, we aim to provide a satisfactory search experience so users can find the vehicles quickly they are looking for. We make it happen using our machine learning systems working 24X7 in the backend which continuously learns changing user interests and optimize the search experience. Based on techniques like learning to rank using XGBoost, this talk will discuss our current search relevance ranking framework and how it ranks millions of searches daily. https://www.youtube.com/watch?v=cwDnYWcMJ1c https://www.youtube-nocookie.com/embed/cwDnYWcMJ1c 2024-06-23 yes https://pycon.de/archive/2024/talks/beyond-deployment-exploring-machine-learning-inference-architectures-and-patterns/ 2024-10-28 https://pycon.de/static/media/social/talks/ZLDMGM.png Beyond Deployment: Exploring Machine Learning Inference Architectures and Patterns https://i.ytimg.com/vi/_OW3ijN7Qc0/maxresdefault.jpg Beyond Deployment: Exploring Machine Learning Inference Architectures and Patterns This talk is about setting up robust and scalable machine learning systems for high-throughput real-time predictions and large numbers of users. It is meant for ML engineers and people who work with data and want to learn more about MLOps focusing on cloud-based platforms. The focus of this talk will be about different ways to make predictions -– real-time, asynchronously and batch processing. It discusses the advantages and disadvantages of the different patterns and highlights the importance of choosing the right pattern for specific use cases, including generative large language models We will use examples from StepStone's production systems to illustrate how to build systems that scale to thousands of simultaneous requests while delivering low-latency, robust predictions. I will cover some of the technical details, how to efficiently manage operations, and real-life examples in a way that is easy to understand and informative. You will learn about different setups for ML and how to make them work. This will help you make your ML inference faster, more cost-efficient, and reliable. https://www.youtube.com/watch?v=_OW3ijN7Qc0 https://www.youtube-nocookie.com/embed/_OW3ijN7Qc0 2024-10-28 yes https://pycon.de/archive/2024/talks/boost-your-app-to-flash-speed-by-mastering-performance-tricks/ 2024-10-28 https://pycon.de/static/media/social/talks/C9F9CC.png Boost your app to Flash speed by mastering performance tricks https://i.ytimg.com/vi/q4WICGF7pL0/maxresdefault.jpg Boost your app to Flash speed by mastering performance tricks In this talk, we discuss computational operations and memory utilization in Python and what is the connection between them. Additionally, we will provide you with visual aids for helping to build a mental picture of these concepts. Moreover, we will dive into how Python interpreter works and how the understanding of bytecode instructions can help you write better code. In the end, we will demonstrate the advantages of best practices by comparing both performance metrics and bytecode instructions. https://www.youtube.com/watch?v=q4WICGF7pL0 https://www.youtube-nocookie.com/embed/q4WICGF7pL0 2024-10-28 yes https://pycon.de/archive/2024/talks/boost-your-data-science-skills-with-the-new-python-in-excel/ 2024-06-23 https://pycon.de/static/media/social/talks/UPSJEM.png Boost your Data Science skills with the new Python in Excel https://i.ytimg.com/vi/grnEdWkcchY/maxresdefault.jpg Boost your Data Science skills with the new Python in Excel Python in Excel is the new integration created by Microsoft that brings Python programming directly into Excel workbooks, for advanced data analytics. With Python in Excel, it is now possible to embed Python code directly into workbook cells, very easily, and with zero setup required. In this tutorial, we will explore the many features and capabilities this new integration provides, to unlock unprecedented data science and machine learning use cases in Excel. https://www.youtube.com/watch?v=grnEdWkcchY https://www.youtube-nocookie.com/embed/grnEdWkcchY 2024-06-23 yes https://pycon.de/archive/2024/talks/breaking-ai-boundaries-fairness-metrics-in-unstructured-data-domains/ 2024-06-23 https://pycon.de/static/media/social/talks/QLXUHY.png Breaking AI Boundaries: Fairness Metrics in Unstructured Data Domains https://i.ytimg.com/vi/-nRjG9nF5S4/maxresdefault.jpg Breaking AI Boundaries: Fairness Metrics in Unstructured Data Domains This presentation addresses the rare use of machine learning fairness metrics in domains with indirect human impact, e.g., automotive engineering. We briefly map out the space of use cases to examine the necessity, potential benefits, and challenges of applying fairness-related techniques. The main focus then lies on proposing solutions for overcoming identified hurdles, especially regarding the application in unstructured data domains, such as image and audio recognition and large text document analysis. Our approach includes strategies for detecting key subgroups and providing clear explanations for model failures. We also highlight two open-source tools, Sliceguard and Spotlight, for practical implementation. https://www.youtube.com/watch?v=-nRjG9nF5S4 https://www.youtube-nocookie.com/embed/-nRjG9nF5S4 2024-06-23 yes https://pycon.de/archive/2024/talks/bridging-the-gap-from-analytical-models-to-operational-success/ 2024-06-23 https://pycon.de/static/media/social/talks/XNY3HX.png Bridging the Gap: From Analytical Models to Operational Success https://i.ytimg.com/vi/BSSxjcgCN8o/maxresdefault.jpg Bridging the Gap: From Analytical Models to Operational Success Deploying machine learning models in production carries its own unique set of challenges. Some challenges stem from different, and sometimes conflicting, objectives between analytics and production. Others arise from technological limitations, business requirements, and even regulatory needs. In this talk, we will focus on the part of the problem surrounding the handover of models from analytics to production. We expect data scientists, operation specialists, and product owners to benefit from our stories. https://www.youtube.com/watch?v=BSSxjcgCN8o https://www.youtube-nocookie.com/embed/BSSxjcgCN8o 2024-06-23 yes https://pycon.de/archive/2024/talks/bridging-the-worlds-pixi-reimplements-pip-and-conda-in-rust/ 2024-10-28 https://pycon.de/static/media/social/talks/HSJGHH.png Bridging the worlds: pixi reimplements pip and conda in Rust https://i.ytimg.com/vi/sVc0PmeSUNk/maxresdefault.jpg Bridging the worlds: pixi reimplements pip and conda in Rust Pixi is a modern package manager that bridges the worlds of conda and pip package management. A from-scratch implementation of a SAT solver that works for both pip and conda, native lockfiles and a cross-platform task system are compelling features of this new package manager. https://www.youtube.com/watch?v=sVc0PmeSUNk https://www.youtube-nocookie.com/embed/sVc0PmeSUNk 2024-10-28 yes https://pycon.de/archive/2024/talks/build-a-personalized-bitcoin-btc-virtual-assistant-in-python-with-hopsworks-and-llm-function-calling/ 2024-06-23 https://pycon.de/static/media/social/talks/JRRET3.png Build a personalized Bitcoin (BTC) virtual assistant in Python with Hopsworks and LLM function calling https://i.ytimg.com/vi/OyMoRMnh8bw/maxresdefault.jpg Build a personalized Bitcoin (BTC) virtual assistant in Python with Hopsworks and LLM function calling The human ambitious desire to get rich without effort has been a major driving force behind the popularity of cryptocurrencies like Bitcoin and Ethereum. However, their high volatility makes them too unpredictable, and keeping track of our investment gains and losses over time can be tedious, if not boring. In this talk, we will define the different components necessary to build a personalized Bitcoin (BTC) virtual assistant in Python. The assistant will help you analyze your transaction history, estimate future BTC prices, and calculate the future value of your holdings based on these predictions. It will be powered by LLMs and will make use of a recent technique called Function Calling to recognize the user intent from the conversation history. https://www.youtube.com/watch?v=OyMoRMnh8bw https://www.youtube-nocookie.com/embed/OyMoRMnh8bw 2024-06-23 yes https://pycon.de/archive/2024/talks/build-an-ai-document-inquiry-chat-with-offline-llms/ 2024-06-23 https://pycon.de/static/media/social/talks/WPKRCT.png Build an AI Document Inquiry Chat with Offline LLMs https://i.ytimg.com/vi/MIzPl3hB1nI/maxresdefault.jpg Build an AI Document Inquiry Chat with Offline LLMs As we descend from the peak of the hype cycle around Large Language Models (LLMs), chat-based document inquiry systems have emerged as a high-value practical use case. Retrieval-Augmented Generation (RAG) is a technique to share relevant context and external information (retrieved from vector storage) to LLMs, thus making them more powerful and accurate. In this hands-on tutorial, we’ll dive into RAG by creating a personal chat app that accurately answers questions about your selected documents. We’ll use a new [OSS project called Ragna](https://ragna.chat/en/latest/) that provides a friendly Python and REST API, designed for this particular case. We’ll test the effectiveness of different LLMs and vector databases, including an offline LLM (i.e., local LLM) running on GPUs on the cloud-machines provided to you. And, we’ll conclude by demonstrating how to quickly build personal or company-level chat-based document interrogation systems. https://www.youtube.com/watch?v=MIzPl3hB1nI https://www.youtube-nocookie.com/embed/MIzPl3hB1nI 2024-06-23 yes https://pycon.de/archive/2024/talks/build-tiktok-s-personalized-real-time-recommendation-system-in-python-with-hopsworks/ 2024-06-23 https://pycon.de/static/media/social/talks/DPGRGW.png Build TikTok's Personalized Real-Time Recommendation System in Python with Hopsworks https://i.ytimg.com/vi/skZ1HcF7AsM/maxresdefault.jpg Build TikTok's Personalized Real-Time Recommendation System in Python with Hopsworks The real-time recommendations engine in Tiktok, Monolith, is so good it has been described as "digital crack" (by Andrej Karpathy, former head of AI at Tesla). In this tutorial, we will build the core components of Tiktok Monolith (a retrieval and ranking architecture): a stream processing feature pipeline, a two-tower embedding model to support personalized queries based on each user's history/context, and a simple user interface in Python (Streamlit). Our real-time machine learning system will consist of 3 Python programs - the feature pipeline, the training pipeline, and the online inference pipeline - and the ML infrastructure they require will be provided by the open-source Hopsworks platform, including a feature store, vector database, model serving, and model registry. https://www.youtube.com/watch?v=skZ1HcF7AsM https://www.youtube-nocookie.com/embed/skZ1HcF7AsM 2024-06-23 yes https://pycon.de/archive/2024/talks/building-accessible-documentation-sites/ 2024-10-28 https://pycon.de/static/media/social/talks/7UYHYP.png Building accessible documentation sites https://i.ytimg.com/vi/2voKwaCuhw4/maxresdefault.jpg Building accessible documentation sites Your project's documentation site is one of the first places where new users will interact with your project; as such, it is essential that these are up-to-date, well-organised, and usable and that they cater to newcomers, experienced users, and contributors alike. It is estimated that about 25% of the global population has some sort of disability, and ensuring all folks can use and access your projects and their documentation is paramount and this, of course, includes thinking of and including disabled developers and end-users. In this talk, we will cover some of the basics of web content accessibility and explore some tools and approaches that you can use to ensure your tools and documentation sites are accessible. https://www.youtube.com/watch?v=2voKwaCuhw4 https://www.youtube-nocookie.com/embed/2voKwaCuhw4 2024-10-28 yes https://pycon.de/archive/2024/talks/building-professional-voice-ai-with-vocode/ 2024-06-23 https://pycon.de/static/media/social/talks/8W7RPP.png Building Professional Voice AI with Vocode https://i.ytimg.com/vi/RS0gn2beLlY/maxresdefault.jpg Building Professional Voice AI with Vocode Dive into the world of AI voice agents with Vocode, the leading framework for creating interactive, voice-based AI assistants. In this talk, we'll explore how Vocode integrates speech-to-text, response generation, and speech synthesis APIs to create agents that not only speak but also understand and adapt to the nuances of human conversation. We'll discuss the challenges of teaching these agents the etiquette of real conversations, such as knowing when to pause, not interrupt, and conclude interactions. Plus, we'll showcase Vocode's LLM function-calling feature through a practical example: real-time appointment booking. Join us to uncover the secrets behind building AI voice agents that are as engaging and efficient as they are innovative. https://www.youtube.com/watch?v=RS0gn2beLlY https://www.youtube-nocookie.com/embed/RS0gn2beLlY 2024-06-23 yes https://pycon.de/archive/2024/talks/bulletproof-python-property-based-testing-with-hypothesis/ 2023-12-21 https://pycon.de/static/media/social/talks/KCC9EF.png Bulletproof Python - Property-Based Testing with Hypothesis https://pycon.de/archive/2024/talks/can-chatgpt-convince-you-to-get-a-covid19-vaccine-comparing-chatgpt-to-an-expert-system-which-one-is-more-convincing/ 2024-06-23 https://pycon.de/static/media/social/talks/BJUQ9E.png Can ChatGPT convince you to get a COVID19 vaccine? Comparing ChatGPT to an expert system - which one is more convincing? https://i.ytimg.com/vi/iCvfpyej0ss/maxresdefault.jpg Can ChatGPT convince you to get a COVID19 vaccine? Comparing ChatGPT to an expert system - which one is more convincing? This study explores the efficacy of chatbots as dialogical argumentation systems for behaviour change, focusing on vaccine hesitancy during the COVID-19 pandemic. A Python-based chatbot, developed in 2021, engaged in argumentative dialogues with users reluctant to get vaccinated, resulting in a 20% positive change in participants' stances. As natural language processing technologies, like ChatGPT, advance, it is crucial to compare them to traditional expert systems. Prior studies have shown ChatGPT's reliability in addressing vaccine hesitancy. This research compares our chatbot with ChatGPT, evaluating persuasiveness through crowdsourced participants. The findings inform resource allocation decisions, guiding the choice between domain-specific expert systems and enhancing versatile models like ChatGPT. Understanding comparative strengths aids in preventing the dissemination of misinformation in behaviour change contexts. https://www.youtube.com/watch?v=iCvfpyej0ss https://www.youtube-nocookie.com/embed/iCvfpyej0ss 2024-06-23 yes https://pycon.de/archive/2024/talks/climate-crisis-in-numbers/ 2024-06-23 https://pycon.de/static/media/social/talks/GNK3PV.png Climate Crisis in Numbers https://i.ytimg.com/vi/MdT4FpQ8grE/maxresdefault.jpg Climate Crisis in Numbers Climate change is one of the biggest and most daunting challenges that our and future generations are going to face. In order to mitigate climate change and its consequences, first one needs to understand the problem and get a rough idea about the magnitude of human made global warming. As a proper numbers nerd I understand problems best when looking at science, statistics, and measurements. So here’s my little guide to better grasp what climate change is all about through data. https://www.youtube.com/watch?v=MdT4FpQ8grE https://www.youtube-nocookie.com/embed/MdT4FpQ8grE 2024-06-23 yes https://pycon.de/archive/2024/talks/cloud-no-thanks-im-gonna-run-genai-on-my-ai-pc/ 2024-06-23 https://pycon.de/static/media/social/talks/BNFLZB.png Cloud? No Thanks! I’m Gonna Run GenAI on My AI PC https://i.ytimg.com/vi/lE6CjjY60vw/maxresdefault.jpg Cloud? No Thanks! I’m Gonna Run GenAI on My AI PC In this speech, we want to introduce an AI PC, a single machine that consists of a CPU, GPU, and NPU (Neural Processing Unit) and can run GenAI in seconds, not hours. Besides the hardware, we will also show the OpenVINO Toolkit, a software solution that helps squeeze as much as possible out of that PC. Join our talk and see for yourself the AI PC is good for both generative and conventional AI models. All presented demos are open source and available on our GitHub. https://www.youtube.com/watch?v=lE6CjjY60vw https://www.youtube-nocookie.com/embed/lE6CjjY60vw 2024-06-23 yes https://pycon.de/archive/2024/talks/community-conferences-under-the-hood-perspectives-and-best-practices-in-volunteer-organization/ 2024-10-28 https://pycon.de/static/media/social/talks/PLJKUH.png Community Conferences under the Hood. Perspectives and Best Practices in Volunteer Organization https://i.ytimg.com/vi/WRhAUBwWEgU/maxresdefault.jpg Community Conferences under the Hood. Perspectives and Best Practices in Volunteer Organization PyCon DE & PyData Berlin is volunteer run. This session aims to underscore the significant role that volunteer organization plays in cultivating environments of authenticity, inclusion, and diversity within tech communities. https://www.youtube.com/watch?v=WRhAUBwWEgU https://www.youtube-nocookie.com/embed/WRhAUBwWEgU 2024-10-28 yes https://pycon.de/archive/2024/talks/connecting-batteries-with-python-towards-ev-charging-with-zero-emissions-at-zero-costs/ 2024-10-28 https://pycon.de/static/media/social/talks/K8AL9P.png Connecting batteries with Python: Towards EV Charging with #zero emissions at #zero costs https://i.ytimg.com/vi/81jNDVGtzvU/maxresdefault.jpg Connecting batteries with Python: Towards EV Charging with #zero emissions at #zero costs This talk dives into how Python helps us to bridge the gap between automotive and energy industries. Learn how Python helps in integrating EV batteries into the power grid, enabling further use and growth of renewable energies, stabilizing power grids and enhancing the accessibility of electric mobility. https://www.youtube.com/watch?v=81jNDVGtzvU https://www.youtube-nocookie.com/embed/81jNDVGtzvU 2024-10-28 yes https://pycon.de/archive/2024/talks/content-recommendation-with-graphs-from-basic-walks-to-neural-networks/ 2024-06-23 https://pycon.de/static/media/social/talks/RD9SU8.png Content Recommendation with Graphs: From Basic Walks to Neural Networks https://i.ytimg.com/vi/IZi9LPAZjl8/maxresdefault.jpg Content Recommendation with Graphs: From Basic Walks to Neural Networks Discover how graph algorithms are transforming content recommendation in this insightful talk. We'll journey from the basics of graph-based models, exploring simple graph walks, to the cutting-edge realm of Graph Neural Networks. Uncover the power of graph embeddings and learn when graph-based approaches excel in recommender systems. https://www.youtube.com/watch?v=IZi9LPAZjl8 https://www.youtube-nocookie.com/embed/IZi9LPAZjl8 2024-06-23 yes https://pycon.de/archive/2024/talks/data-valuation-for-machine-learning/ 2024-06-23 https://pycon.de/static/media/social/talks/WNHAG8.png Data valuation for machine learning https://i.ytimg.com/vi/DRBxJ65C7jI/maxresdefault.jpg Data valuation for machine learning Data valuation techniques compute the contribution of training points to the final performance of machine learning models. They are part of so-called data-centric ML, with immediate applications in data engineering like data pruning or improved collection processes, and in model debugging and development. In this talk we demonstrate how the open source library [pyDVL](https://pydvl.org) can be used to detect mislabeled and out-of-distribution samples with little effort. We cover the core ideas behind the most successful algorithms and illustrate how they can be used to inspect your data to extract the most out of it. https://www.youtube.com/watch?v=DRBxJ65C7jI https://www.youtube-nocookie.com/embed/DRBxJ65C7jI 2024-06-23 yes https://pycon.de/archive/2024/talks/ddataflow-an-open-source-end-to-end-testing-framework-for-ml-pipelines/ 2024-10-28 https://pycon.de/static/media/social/talks/8GQLLY.png DDataflow: An open-source end-to-end testing framework for ML pipelines https://i.ytimg.com/vi/F01NrNN3zv4/maxresdefault.jpg DDataflow: An open-source end-to-end testing framework for ML pipelines In the realm of machine learning, the complexity of data pipelines often hinders rapid experimentation and iteration. This talk will introduce [DDataflow](https://github.com/getyourguide/DDataFlow), an innovative open-source tool, designed to facilitate end-to-end testing in ML pipelines by leveraging decentralized data sampling. Attendees will gain insights into the challenges of unit testing in large-scale data pipelines, the design philosophy behind DDataflow, and practical implementation strategies to enhance the reliability and efficiency of their ML pipelines. https://www.youtube.com/watch?v=F01NrNN3zv4 https://www.youtube-nocookie.com/embed/F01NrNN3zv4 2024-10-28 yes https://pycon.de/archive/2024/talks/deploying-your-python-application-to-android/ 2024-10-28 https://pycon.de/static/media/social/talks/W7YDRX.png Deploying your Python application to Android https://i.ytimg.com/vi/ntSsc6wHnJU/maxresdefault.jpg Deploying your Python application to Android Since many years Android has held the top position as the most used OS with about 38% of the OS user share in 2023. Currently 3 major languages – C++, Java, Kotlin are used for application development on Android. Although Python has the capabilities of enabling Android deployment, Python was never considered as an adequate language for Android development. But, with the introduction of “PEP 738: Adding Android as a supported platform”, and the increasing popularity of frameworks like PySide6, Kivy, Flet etc. which enable GUI development with Python for Android devices, it is time for Python package developers to consider Android as a potential platform. This talk gives an introduction to each of the GUI development toolkits – Kivy, Flet and PySide6 by demonstrating how to create a simple Contact List application. We later delve into the pros and cons of each of these frameworks, so that Python application developers can decide which framework suits their requirements better. https://www.youtube.com/watch?v=ntSsc6wHnJU https://www.youtube-nocookie.com/embed/ntSsc6wHnJU 2024-10-28 yes https://pycon.de/archive/2024/talks/django-an-asynchronous-microservices-technique/ 2024-10-28 https://pycon.de/static/media/social/talks/7NETLX.png µDjango, an asynchronous microservices technique. https://i.ytimg.com/vi/jBGhhsPdRNs/maxresdefault.jpg µDjango, an asynchronous microservices technique. A standard Django project involves working with multiple files and folders from the start. Let's see how the work with a Django project changes when we have only one file. This solution automatically transforms Django into a microservice-oriented async framework with "batteries included” philosophy. https://www.youtube.com/watch?v=jBGhhsPdRNs https://www.youtube-nocookie.com/embed/jBGhhsPdRNs 2024-10-28 yes https://pycon.de/archive/2024/talks/django-loves-strawberries/ 2024-10-28 https://pycon.de/static/media/social/talks/AT9HCG.png Django loves strawberries https://i.ytimg.com/vi/EQwF5xUi4Oc/maxresdefault.jpg Django loves strawberries Explore the dynamic duo of GraphQL Strawberry and Django in an immersive workshop! Discover the seamless integration of Strawberry with Django, mastering type definitions, queries and mutations. Harness the power of Starlette for efficient API development, empowering your projects with this potent blend of cutting-edge technologies. https://www.youtube.com/watch?v=EQwF5xUi4Oc https://www.youtube-nocookie.com/embed/EQwF5xUi4Oc 2024-10-28 yes https://pycon.de/archive/2024/talks/documenting-r-d-progress-using-jupyter-book-and-feel-safe-for-the-next-performance-audit/ 2024-06-23 https://pycon.de/static/media/social/talks/YYKJMP.png Documenting R&D Progress using jupyter-book - and feel safe for the next performance audit https://i.ytimg.com/vi/lxXlJ-ZPxrI/maxresdefault.jpg Documenting R&D Progress using jupyter-book - and feel safe for the next performance audit Rosenxt has only just been founded, and yet we are already very busy researching great things and making them usable. The ideas are bubbling, the motivation is high. The urge to try out the next idea quickly is high. But progress needs to be well documented, as the next performance audit is sure to come. https://www.youtube.com/watch?v=lxXlJ-ZPxrI https://www.youtube-nocookie.com/embed/lxXlJ-ZPxrI 2024-06-23 yes https://pycon.de/archive/2024/talks/encoding-charactersets-may-the-force-be-with-you/ 2024-10-28 https://pycon.de/static/media/social/talks/RRAZ99.png Encoding Charactersets - may the force be with you https://i.ytimg.com/vi/XR5rmoDa-rc/maxresdefault.jpg Encoding Charactersets - may the force be with you Understanding and repairing garbled text (Mojibake) is despite Unicode a permanent ongoing task in IT projects. Garbled text is the result of text being decoded using an unintended character encoding. Example: Die UTF-8 Selbsthilfegruppe trifft sich heute Abend im grünen Saal This talks explains how to analyze and fix such encoding problems with python. The topics of this talk contains: - difference between grapheme and codepoints - Unicode vs. UTF-8 - decoding and encoding files, database result sets, REST-APIs calls - the unicodedata module - handling of ISO charsets in the unicode world This talk shows short code examples for real world problems and solutions. https://www.youtube.com/watch?v=XR5rmoDa-rc https://www.youtube-nocookie.com/embed/XR5rmoDa-rc 2024-10-28 yes https://pycon.de/archive/2024/talks/enhance-your-balcony-power-plant-with-python/ 2024-10-28 https://pycon.de/static/media/social/talks/TCSERC.png Enhance your balcony power plant with Python https://i.ytimg.com/vi/5CiDAmKi-k4/maxresdefault.jpg Enhance your balcony power plant with Python Plug-in solar systems, so-called balcony power plants, are getting more popular. This talk will cover the basics of such a system, how to figure out the energy consumption of a household and how to monitor and optimize the power output of a balcony power plant. https://www.youtube.com/watch?v=5CiDAmKi-k4 https://www.youtube-nocookie.com/embed/5CiDAmKi-k4 2024-10-28 yes https://pycon.de/archive/2024/talks/everything-you-need-to-know-about-change-point-detection/ 2023-12-26 https://pycon.de/static/media/social/talks/ZKYA9W.png Everything you need to know about change-point detection https://pycon.de/archive/2024/talks/exploring-zarr-from-fundamentals-to-version-3-0-and-beyond/ 2024-06-23 https://pycon.de/static/media/social/talks/93MHQ3.png Exploring Zarr: From Fundamentals to Version 3.0 and Beyond https://i.ytimg.com/vi/05uyjOP_8OU/maxresdefault.jpg Exploring Zarr: From Fundamentals to Version 3.0 and Beyond A key feature of the Python data ecosystem is the reliance on simple but efficient primitives that follow well-defined interfaces to make tools work seamlessly together (Cf. http://data-apis.org/). NumPy provides an in-memory representation for tensors. Dask provides parallelisation of tensor access. Xarray provides metadata linking tensor dimensions. **Zarr** provides a missing feature, namely the scalable, persistent storage for annotated hierarchies of tensors. Defined through a community process, the Zarr specification enables the storage of large out-of-memory datasets locally and in the cloud. Implementations exist in C++, C, Java, Javascript, Julia, and Python, enabling. This talk presents a systematic approach to understanding the newer [Zarr Specification Version 3](https://zarr-specs.readthedocs.io/en/latest/v3/core/v3.0.html) by explaining the critical design updates, performance improvements, and the lessons learned via the broader specification adoption across the scientific ecosystem. I will also briefly discuss the evolution of the Zarr - the development of the [Zarr Enhancement Process (ZEP)](https://zarr.dev/zeps) and its use to define the next major version of the specification (V3); as well as uptake of the format across the research landscape. https://www.youtube.com/watch?v=05uyjOP_8OU https://www.youtube-nocookie.com/embed/05uyjOP_8OU 2024-06-23 yes https://pycon.de/archive/2024/talks/flixbus-citysnap-how-we-use-genai-and-not-only-to-collect-captivating-images-for-cities-and-confirm-their-locations/ 2024-06-23 https://pycon.de/static/media/social/talks/ECCJAG.png FlixBus CitySnap: How we use GenAI and not only to collect captivating images for cities and confirm their locations https://i.ytimg.com/vi/BYbCihB3kLI/maxresdefault.jpg FlixBus CitySnap: How we use GenAI and not only to collect captivating images for cities and confirm their locations Have you ever wondered how travel e-commerce companies gather photos of cities? While I can't speak for everyone, I will demonstrate the innovative approach we are using at Flix. In recent years, text-to-text models like ChatGPT and text-to-image models such as DALL-E 3 have become increasingly integrated into various industries. The main aim of these initiatives is typically to generate text or images. In our presentation, we propose a slightly different approach to leveraging these models commercially. Our objective is to gather images for thousands of cities that inspire travel. We utilize ChatGPT to tailor prompts for our business requirements, enabling efficient image retrieval through API queries from free stock image services. Then we apply image-to-text models to confirm the images' locations. Finally, we need to adjust the resolution of images for display across various platforms, such as social media campaigns on Instagram, email marketing, and on our website. To achieve this, we have used an automated cropping service to get images in the required aspect ratios, followed by Lanczos sampling for downscaling the images. This integration of cutting-edge models has resulted in an automated, highly flexible process that aligns with varied business needs. Our approach is cost-efficient; processing several hundred cities amounts to only a few euros, and we have utilized commonly available services, making replication easy for everyone. https://www.youtube.com/watch?v=BYbCihB3kLI https://www.youtube-nocookie.com/embed/BYbCihB3kLI 2024-06-23 yes https://pycon.de/archive/2024/talks/from-idea-to-production-in-a-day-leveraging-azure-ml-and-streamlit-to-build-and-user-test-machine-learning-ideas-quickly/ 2024-06-23 https://pycon.de/static/media/social/talks/GVTJW8.png From idea to production in a day: Leveraging Azure ML and Streamlit to build and user test machine learning ideas quickly https://i.ytimg.com/vi/56LFY0bVm4A/maxresdefault.jpg From idea to production in a day: Leveraging Azure ML and Streamlit to build and user test machine learning ideas quickly Getting a machine learning solution in front of users usually takes some time. The data science tech stack is full of time traps and infrastructure issues might slow down deployment. The Azure Machine Learning platform, automated machine learning, and Streamlit are predestined tools for circumventing common development and deployment issues – if you know how to use them. Based on our learnings in corporate hackathons, we will use the stack to rapidly prototype a computer vision application users can interact with. You will walk away with Python code snippets and inspiration to build and user test your own machine learning ideas quickly. https://www.youtube.com/watch?v=56LFY0bVm4A https://www.youtube-nocookie.com/embed/56LFY0bVm4A 2024-06-23 yes https://pycon.de/archive/2024/talks/from-llm-as-oracle-to-llm-as-translator-our-journey-from-theory-to-everydays-practice-in-a-corporate-setting-with-dmgpt-and-python/ 2023-12-21 https://pycon.de/static/media/social/talks/P3GRLG.png From LLM as oracle to LLM as translator - our journey from theory to everyday’s practice in a corporate setting with dmGPT (and python) https://pycon.de/archive/2024/talks/functional-python/ 2024-10-28 https://pycon.de/static/media/social/talks/PKJHBA.png Functional Python https://i.ytimg.com/vi/Xhexo03nYko/maxresdefault.jpg Functional Python Python supports multiple programming paradigms. In addition to the procedural and object-oriented approach, it also provides some features that are typical for functional programming. While these features are optional, they can be useful to create better Python programs. This tutorial introduces Python features that help to implement parts of Python programs in the functional style. Objective is not to write pure functional programs but improve programs design by using functional feature where suitable. The tutorial points out advantages and disadvantages of functional programming in general and in Python in particular. Participants will learn alternative ways to solve problems. This will broaden their programming toolbox. https://www.youtube.com/watch?v=Xhexo03nYko https://www.youtube-nocookie.com/embed/Xhexo03nYko 2024-10-28 yes https://pycon.de/archive/2024/talks/going-beyond-parquet-s-default-settings-be-surprised-what-you-can-get/ 2024-06-23 https://pycon.de/static/media/social/talks/SQUNWS.png Going beyond Parquet's default settings – be surprised what you can get https://i.ytimg.com/vi/GkBDqTrV0hg/maxresdefault.jpg Going beyond Parquet's default settings – be surprised what you can get Apache Parquet has become the de facto format for storing tabular (DataFrame) data on disk. This is done through universal compression and efficient knowledge of the stored data structure. As part of this talk, we would like to show the core structure of Parquet and the knobs that allow you to get even more of the capabilities of the file format. https://www.youtube.com/watch?v=GkBDqTrV0hg https://www.youtube-nocookie.com/embed/GkBDqTrV0hg 2024-06-23 yes https://pycon.de/archive/2024/talks/green-software-engineering/ 2024-10-28 https://pycon.de/static/media/social/talks/Z3FALV.png Green Software Engineering https://i.ytimg.com/vi/vTIkZwrrIwM/maxresdefault.jpg Green Software Engineering Did this question ever cross your mind that how green software engineering can help in environment sustainability? My talk will answer this exact question.  My passion for nature and love for technology pushed me into this topic. The way global warming is affecting us is one of the biggest concern of so many people around the world. The focus is to educate people about how they can play their role in protecting the environment by just using their laptop or computers in the right possible way. One of the biggest questions is to deal with the gas emissions and control it but how software engineering can help in all of this? The complete cycle of the Software Engineering should be designed and implemented in such a way that it incorporates environment sustainability without affecting the economic benefits. It is a win win situation. We need more environment sustainable mobile and web applications. https://www.youtube.com/watch?v=vTIkZwrrIwM https://www.youtube-nocookie.com/embed/vTIkZwrrIwM 2024-10-28 yes https://pycon.de/archive/2024/talks/haystack-2-0-the-story-of-a-rewrite/ 2024-06-23 https://pycon.de/static/media/social/talks/GLXJPC.png Haystack 2.0: the story of a rewrite https://i.ytimg.com/vi/0CNSvGlMNSo/maxresdefault.jpg Haystack 2.0: the story of a rewrite To rewrite or not to rewrite: it's a major question. Releasing new software versions with breaking changes can be disruptive to a community, but sometimes they are necessary in the long run to move forward. Haystack is a free open source Python LLM framework. It was launched in 2020, before LLMs were cool. In 2023 we decided to undergo a major re-architecture, culminating in the GA release of Haystack 2.0. It wasn't an easy decision. By involving the open source community and some big companies in our design process early on, we are confident we built a more usable, flexible foundation for years to come. In this talk I'll tell you the story of this rewrite. The decisions we made to bring the project forward with the right level of flexibility / composability in the rapidly changing LLM landscape. I won't only show you the new features 2.0 provides, but give you a peek into our future roadmap. You'll walk away with a better understanding of how modern LLM frameworks can help you solve problems for yourself and your users, as well as an enriched understanding of how to think for the long-term when building for an open source community. You’ll see how the strength of Haystack modularity and ease of use makes it stand out from other libraries. Demos will make it much clear and give you some great ideas on how to integrate Haystack in your projects. https://www.youtube.com/watch?v=0CNSvGlMNSo https://www.youtube-nocookie.com/embed/0CNSvGlMNSo 2024-06-23 yes https://pycon.de/archive/2024/talks/high-performance-data-visualization-for-the-web/ 2024-06-23 https://pycon.de/static/media/social/talks/9JEZ8E.png High Performance Data Visualization for the Web https://i.ytimg.com/vi/v5Y5ftlGNhU/maxresdefault.jpg High Performance Data Visualization for the Web In this talk, we will put together a simple but full-featured website using [Perspective](https://perspective.finos.org). Perspective is an open source interactive analytics and data visualization component, which is especially well-suited for large and/or streaming datasets. It is written in C++ and Rust with bindings to both Python and WebAssembly, making it ideal for data-intensive applications. It comes with a variety of visualization plugins, including a datagrid and various charts. Additionally, it comes with a Jupyter widget, which allows developers to iterate quickly with a clear pathway to their production website. https://www.youtube.com/watch?v=v5Y5ftlGNhU https://www.youtube-nocookie.com/embed/v5Y5ftlGNhU 2024-06-23 yes https://pycon.de/archive/2024/talks/how-python-helped-us-uncover-secrets-of-protein-motion/ 2024-06-23 https://pycon.de/static/media/social/talks/TMF8V7.png How Python helped us uncover secrets of protein motion https://i.ytimg.com/vi/2z66DLkue9c/maxresdefault.jpg How Python helped us uncover secrets of protein motion This presentation will give an overview of the scientific project that focuses on understanding how proteins move and function. Along the way a very large collection of Python tools was used, and on top of them our own innovative approaches are based. To be able to understand everything about living beings, including our health and origin of deseases in humans, we have to know how proteins do what they do. Hence is of utmost importance to understand their structure and function. Thanks to extraordinary technique called X-ray crystallography we are able to see how the proteins look at atomic scale, but it is impossible to see how they move. Therefore the next best thing we can do is to simulate the motion of the protein by so-called molecular dynamics (MD) simulations. These simulations generate incredible amounts of data, generally hundreds of GB of data per 1 microsecond of protein movement! Extracting useful and meaningful information from it is a daunting task. We are going to show how we have used many Python tools to tackle this problem in the project. Using Django to place everything in an interactive web app (https://alokomp.irb.hr/), along with Pandas, Numpy, Scipy, Dask, Jupyther, NetworkX, Bokeh, Datashader and many more under the hood, we have created an innovative new way of seeing protein move and communicate. https://www.youtube.com/watch?v=2z66DLkue9c https://www.youtube-nocookie.com/embed/2z66DLkue9c 2024-06-23 yes https://pycon.de/archive/2024/talks/how-to-do-monolingual-multilingual-and-cross-lingual-text-classification-in-april-2024/ 2024-06-23 https://pycon.de/static/media/social/talks/RLCLBB.png How to Do Monolingual, Multilingual, and Cross-lingual Text Classification in April, 2024 https://i.ytimg.com/vi/yFE8N7-uZL8/maxresdefault.jpg How to Do Monolingual, Multilingual, and Cross-lingual Text Classification in April, 2024 In 2023, the field of NLP was again flurried -- the appearing of powerful closed- and opens-source LLMs opened new possibility for texts processing. However, many questions about these models usability for typical NLP tasks are still open. One of them is quite simple -- if we want a classification model for some task, can we rely on LLMs or is it still better to fine-tune an own model? It might be easier to obtain some classifier for English, but what if my target language is not so resource-rich? In this presentation, the main "recipes" how to obtain the best text classifier depending on the language and data availability will be described. https://www.youtube.com/watch?v=yFE8N7-uZL8 https://www.youtube-nocookie.com/embed/yFE8N7-uZL8 2024-06-23 yes https://pycon.de/archive/2024/talks/how-to-embrace-your-leadership-role-as-a-data-nerd-or-other-creative-types/ 2024-06-23 https://pycon.de/static/media/social/talks/TU9EUQ.png How to embrace your Leadership role as a Data Nerd (or other creative types) https://i.ytimg.com/vi/iQ3bDVmIR6A/maxresdefault.jpg How to embrace your Leadership role as a Data Nerd (or other creative types) The transition from a hands-on creative job to a leadership role isn't always smooth. The tasks you excelled at are now handled by your team, and your new title brings added responsibilities, numerous meetings, leaving little room for deep work. So, how do we— the data people, the coaches, the coders—thrive in management roles? In this talk, I'll share my journey into management and how I learned to embrace and find reward in my leadership role. https://www.youtube.com/watch?v=iQ3bDVmIR6A https://www.youtube-nocookie.com/embed/iQ3bDVmIR6A 2024-06-23 yes https://pycon.de/archive/2024/talks/how-to-improve-the-python-development-experience-for-millions-of-ubuntu-users/ 2024-10-28 https://pycon.de/static/media/social/talks/DKL7YQ.png How to Improve the Python Development Experience for Millions of Ubuntu Users https://i.ytimg.com/vi/WueCxRoMQ_I/maxresdefault.jpg How to Improve the Python Development Experience for Millions of Ubuntu Users Have you ever tried to install a different Python version on Ubuntu or tried to upgrade your current one? Lots of posts exist, many are outdated, and some even lead to a broken Ubuntu installation. This talk will introduce the most common options and their ups and downs in-depth. We will also give an outlook on what Ubuntu could do to make it even easier for you and everybody. https://www.youtube.com/watch?v=WueCxRoMQ_I https://www.youtube-nocookie.com/embed/WueCxRoMQ_I 2024-10-28 yes https://pycon.de/archive/2024/talks/i-achieved-peak-performance-in-python-here-s-how/ 2024-10-28 https://pycon.de/static/media/social/talks/RKDSK7.png I achieved peak performance in python, here's how ... https://i.ytimg.com/vi/wMnU_tR5-84/maxresdefault.jpg I achieved peak performance in python, here's how ... In the ever-evolving landscape of software development, crafting code that not only functions flawlessly but also operates at peak performance is a skill that sets exceptional developers apart. This talk delves into the art of optimizing Python code, exploring techniques and strategies to fine-tune your programs for maximum speed and minimal resource consumption, with a particular focus on memory efficiency. https://www.youtube.com/watch?v=wMnU_tR5-84 https://www.youtube-nocookie.com/embed/wMnU_tR5-84 2024-10-28 yes https://pycon.de/archive/2024/talks/improve-llm-based-applications-with-fallback-mechanisms/ 2024-06-23 https://pycon.de/static/media/social/talks/QCNXLW.png Improve LLM-based Applications with Fallback Mechanisms https://i.ytimg.com/vi/3A9fDBrklP4/maxresdefault.jpg Improve LLM-based Applications with Fallback Mechanisms While RAG addresses the common LLM pitfalls, challenges like handling out-of-domain queries still persist. Learn the significance of fallback mechanisms to tackle these issues gracefully, incorporating strategies like web searches and alternative data sources to improve the user experience of your system. In this session, we’ll discover various fallback techniques and practical implementation using Haystack, empowering you to develop resilient LLM-based systems for diverse scenarios without human intervention. https://www.youtube.com/watch?v=3A9fDBrklP4 https://www.youtube-nocookie.com/embed/3A9fDBrklP4 2024-06-23 yes https://pycon.de/archive/2024/talks/is-genai-all-you-need-to-classify-text-some-learnings-from-the-trenches/ 2024-06-23 https://pycon.de/static/media/social/talks/CWUQF3.png Is GenAI All You Need to Classify Text? Some Learnings from the Trenches https://i.ytimg.com/vi/Y4HQQPfyzwo/maxresdefault.jpg Is GenAI All You Need to Classify Text? Some Learnings from the Trenches In recent times, GenAI has sparked fervent excitement, sometimes touted as the panacea for all natural language processing (NLP) tasks. This presentation explores a practical text classification scenario at Malt, highlighting the practical hurdles encountered when employing GenAI (latency, environmental impact, and budgetary constraints). To overcome these obstacles, a smaller, dedicated model emerged as a viable solution. We'll delve into the construction and optimization (quantization, graph optimization) of this multilingual model. Finally we’ll see how GenAI's unparalleled zero-shot capabilities enables its continuous adaptation. https://www.youtube.com/watch?v=Y4HQQPfyzwo https://www.youtube-nocookie.com/embed/Y4HQQPfyzwo 2024-06-23 yes https://pycon.de/archive/2024/talks/jupyter-notebooks-for-print-media/ 2024-06-23 https://pycon.de/static/media/social/talks/DBGXJN.png Jupyter Notebooks for Print Media https://i.ytimg.com/vi/XEwZ-Dvs21s/maxresdefault.jpg Jupyter Notebooks for Print Media In this talk, we will discuss leveraging Jupyter Notebooks to generate print media - books, magazine and newspaper articles, business reports, academic papers, etc. We will motivate the problem, introduce a library for accomplishing the task (nbprint), and walk through some end-to-end examples. https://www.youtube.com/watch?v=XEwZ-Dvs21s https://www.youtube-nocookie.com/embed/XEwZ-Dvs21s 2024-06-23 yes https://pycon.de/archive/2024/talks/keynote-a-view-from-my-window-an-outside-perspective-of-open-source-scientific-computing-from-the-inside/ 2024-06-23 https://pycon.de/static/media/social/talks/TNHMGN.png Keynote - A View From My Window - An Outside Perspective of Open Source Scientific Computing From the Inside https://i.ytimg.com/vi/NWIuQ3mztTI/maxresdefault.jpg Keynote - A View From My Window - An Outside Perspective of Open Source Scientific Computing From the Inside Twelve years as the Executive Director of NumFOCUS has given me a unique perspective of the open source scientific ecosystem. Building an organization to support project communities has taken me down many roads. Navigating these paths has been rewarding and challenging. We will look at lessons learned as I share my experiences through observations and insights on projects, community leadership, education, and fundraising. https://www.youtube.com/watch?v=NWIuQ3mztTI https://www.youtube-nocookie.com/embed/NWIuQ3mztTI 2024-06-23 yes https://pycon.de/archive/2024/talks/keynote-safe-space-or-trap-creating-software-like-duckdb-in-academic-institutions/ 2024-10-28 https://pycon.de/static/media/social/talks/HKFN8J.png Keynote - Safe Space or Trap? Creating Software like DuckDB in Academic Institutions https://i.ytimg.com/vi/PoHfh6O43uE/maxresdefault.jpg Keynote - Safe Space or Trap? Creating Software like DuckDB in Academic Institutions DuckDB is an in-process analytical data management system. DuckDB is free and open source and rather popular. It is one of the fastest growing data system to date, especially in the Python ecosystem. DuckDB was created at Centrum Wiskunde & Informatica (CWI) in Amsterdam, not entirely coincidentally the same place Python was created in. Later on, the we founded a commercial company, DuckDB Labs, which now drives development. In my talk, I will discuss DuckDB, its origins, and the unique benefits and challenges of maintaining popular software in an academic setting. https://www.youtube.com/watch?v=PoHfh6O43uE https://www.youtube-nocookie.com/embed/PoHfh6O43uE 2024-10-28 yes https://pycon.de/archive/2024/talks/keynote-ten-key-questions-that-a-company-should-ask-to-have-responsible-ai/ 2024-06-23 https://pycon.de/static/media/social/talks/PLRERM.png Keynote - Ten Key Questions that a Company Should Ask to have Responsible AI https://i.ytimg.com/vi/CwYjWSDr_Bo/maxresdefault.jpg Keynote - Ten Key Questions that a Company Should Ask to have Responsible AI Responsible AI covers mainly AI principles, governance & regulation, but most companies do not know how to implement all of these. Hence, in this presentation we cover the key questions for the whole process behind a new AI product, from the idea and design to the development and deployment. The questions are partly based on the new ACM Principles for Responsible Algorithmic Systems (2022) where he is one of the two lead authors as well as their extensions for Generative AI (2023). For each question we will discuss its relevance, challenges, and (partial) solutions, triggering an interactive discussion. https://www.youtube.com/watch?v=CwYjWSDr_Bo https://www.youtube-nocookie.com/embed/CwYjWSDr_Bo 2024-06-23 yes https://pycon.de/archive/2024/talks/keynote-the-art-and-science-of-tending-open-source-orchards/ 2024-06-23 https://pycon.de/static/media/social/talks/7TEYDQ.png Keynote - The art and science of tending open source orchards https://i.ytimg.com/vi/eXJ9RVog-qw/maxresdefault.jpg Keynote - The art and science of tending open source orchards Over the history of free and open source software, we have gone through quite a few metaphors for open source projects: from homesteads in noosphere to puppies, roads & bridges, gardens, forests, and orchards. Regardless of the preferred comparison, we all can agree that behind every large open source project is a resilient contributor community. Is there a blueprint for it? How about a script for scaling a contributor community or a formula for contributor retention? In this talk, I will examine all these questions and share my insight on the art and science of fostering resilient open source communities. https://www.youtube.com/watch?v=eXJ9RVog-qw https://www.youtube-nocookie.com/embed/eXJ9RVog-qw 2024-06-23 yes https://pycon.de/archive/2024/talks/lessons-learned-from-deploying-machine-learning-in-an-old-fashioned-heavy-industry/ 2024-06-23 https://pycon.de/static/media/social/talks/UGJJMP.png Lessons learned from deploying Machine Learning in an old-fashioned heavy industry https://i.ytimg.com/vi/CZqlHH-KZqw/maxresdefault.jpg Lessons learned from deploying Machine Learning in an old-fashioned heavy industry About 5 years ago my co-founder and I launched alcemy, a Machine Learning startup to help decarbonize the cement and concrete supply chain. I experienced first hand moving from a simple proof of concept, a ML model inside a Jupyter notebook, to a full-fledged pipeline running 24/7 and steering massive amounts of cement production in real plants. I can tell you the road was long and winding. I want to share some of the hard lessons we learned along the way with you. If you are an aspiring ML or Software Engineer, Data Scientist, Entrepreneur, or you are just wondering how Machine Learning applied in the wild looks like this talk is for you. No prior knowledge is required except some familiarity with basic concepts and terminology of Machine Learning. https://www.youtube.com/watch?v=CZqlHH-KZqw https://www.youtube-nocookie.com/embed/CZqlHH-KZqw 2024-06-23 yes https://pycon.de/archive/2024/talks/leveraging-the-art-of-parallel-unit-testing-in-django/ 2024-10-28 https://pycon.de/static/media/social/talks/ZKDEPW.png Leveraging the Art of Parallel Unit Testing in Django https://i.ytimg.com/vi/cpu3Sn5NXQU/maxresdefault.jpg Leveraging the Art of Parallel Unit Testing in Django Unit testing is a fundamental practice in software development, ensuring the reliability and maintainability of code. However, in the context of monolith repositories, executing unit tests efficiently becomes a formidable challenge. This conference aims to explore the intricacies of unit testing in Django within monolithic codebases and shed light on how major institutions address and overcome these challenges through the implementation of parallel testing strategies. https://www.youtube.com/watch?v=cpu3Sn5NXQU https://www.youtube-nocookie.com/embed/cpu3Sn5NXQU 2024-10-28 yes https://pycon.de/archive/2024/talks/lose-your-fear-of-equations/ 2024-06-23 https://pycon.de/static/media/social/talks/SYJE7B.png Lose your fear of equations! https://i.ytimg.com/vi/8ScOTAM8joI/maxresdefault.jpg Lose your fear of equations! The skill of quickly judging what a formula does and how changing a parameter will affect the result is crucial when dealing with real-life data science - but it's a skill not easily acquired if you don't come from a STEM background. In this tutorial we'll work on guesstimating what complex mathematical expressions do so that you, too, can lose your fear of math! https://www.youtube.com/watch?v=8ScOTAM8joI https://www.youtube-nocookie.com/embed/8ScOTAM8joI 2024-06-23 yes https://pycon.de/archive/2024/talks/machine-learning-on-microcontrollers-using-micropython-and-emlearn/ 2024-06-23 https://pycon.de/static/media/social/talks/NYHFSB.png Machine Learning on microcontrollers using MicroPython and emlearn https://i.ytimg.com/vi/S3GjLr0ZIE0/maxresdefault.jpg Machine Learning on microcontrollers using MicroPython and emlearn This presentation will show you how to deploy machine learning models to affordable microcontroller-based systems - using the Python that you already know. Combined with sensors, such as microphone, accelerometer or camera, this makes it possible to create devices that can automatically analyze and react to physical phenomena. This enables a wide range of useful and fun applications, and is often referred to as "TinyML". The presentation will cover key concepts and explain the different steps of the process. We will train the machine learning models using standard scikit-learn and Keras, and then execute them on device using the emlearn library. To run Python code on the microcontroller, MicroPython will be used. We will demonstrate some practical use-cases using different sensors, such as Sound Event Detection (microphone), Image Classification (camera), and Human Activity Recognition (accelerometer). https://www.youtube.com/watch?v=S3GjLr0ZIE0 https://www.youtube-nocookie.com/embed/S3GjLr0ZIE0 2024-06-23 yes https://pycon.de/archive/2024/talks/marketing-media-mix-models-with-python-pymc-a-case-study/ 2024-06-23 https://pycon.de/static/media/social/talks/D7AEQY.png Marketing Media Mix Models with Python & PyMC: a Case Study https://i.ytimg.com/vi/k2LDWMLZQ8k/maxresdefault.jpg Marketing Media Mix Models with Python & PyMC: a Case Study In today's digital landscape, traditional analytics struggle with understanding marketing ROI, especially with evolving privacy norms. But Python and its ecosystem come to the rescue. In this talk, we will discuss how we leveraged Python and PyMC to build a Bayesian Marketing Media Mix model for the fastest-growing Italian tour operator. We'll cover the challenges we faced, the valuable insights we gained, and the results achieved. This will offer you a clear and practical roadmap for developing a similar model for your business. https://www.youtube.com/watch?v=k2LDWMLZQ8k https://www.youtube-nocookie.com/embed/k2LDWMLZQ8k 2024-06-23 yes https://pycon.de/archive/2024/talks/missing-data-bayesian-imputation-and-people-analytics-with-pymc/ 2024-06-23 https://pycon.de/static/media/social/talks/KXU7Q8.png Missing Data, Bayesian Imputation and People Analytics with PyMC https://i.ytimg.com/vi/XUo_Z6a4d7c/maxresdefault.jpg Missing Data, Bayesian Imputation and People Analytics with PyMC We demonstrate a range of different approaches to missing data imputation in employee engagement survey data. Contrasting frequentist style full-information maximum likelihood approaches with more direct Bayesian imputation and chained equation methods, we highlight how the different assumptions regarding the missing-data license different inferences about the imputed values and ultimately the plausible causal narratives which can be expressed in PyMC. In particular we avail of the hierarchical nature of employee engagement data to justify a hierarchical approach to justifying the (MAR) missing-at-random assumption for imputation schemes in People Analytics. https://www.youtube.com/watch?v=XUo_Z6a4d7c https://www.youtube-nocookie.com/embed/XUo_Z6a4d7c 2024-06-23 yes https://pycon.de/archive/2024/talks/mojo-is-it-python-s-faster-cousin-or-just-hype/ 2024-10-28 https://pycon.de/static/media/social/talks/DG8G7Q.png Mojo 🔥 - Is it Python's faster cousin or just hype? https://i.ytimg.com/vi/A67P4-A4yLk/maxresdefault.jpg Mojo 🔥 - Is it Python's faster cousin or just hype? On 2023-05-02, the tech sphere buzzed with the release of Mojo 🔥, a new programming language developed by Chris Lattner, renowned for his work on Clang, LLVM, and Swift. Billed as "Python's faster cousin," and "The programming language for all AI developers", Mojo promised a 68,000x performance uplift and a familiar Pythonic syntax. As it reaches its first anniversary, we unpack Mojo's journey towards its ambitious promise. This talk delves into the practical experiences developing a Large Language Model Interpretation library as part of an AI Safety Camp project in that language. We cast a critical eye over its performance, evaluate its usability, and explore its potential as a Python superset. Against a backdrop where alternatives like Rust, PyPy and Julia dominate performant programming for AI, we question whether Mojo can carve out its niche or if it will languish as another "could-have-been" in the programming language pantheon. https://www.youtube.com/watch?v=A67P4-A4yLk https://www.youtube-nocookie.com/embed/A67P4-A4yLk 2024-10-28 yes https://pycon.de/archive/2024/talks/mostly-harmless-fixed-effects-regression-in-python-with-pyfixest/ 2024-06-23 https://pycon.de/static/media/social/talks/UXQJTF.png Mostly Harmless Fixed Effects Regression in Python with PyFixest https://i.ytimg.com/vi/kSQxGGA7Rr4/maxresdefault.jpg Mostly Harmless Fixed Effects Regression in Python with PyFixest This session introduces PyFixest, an open source Python library inspired by the "fixest" R package. PyFixest implements fast routines for the estimation of regression models with high-dimensional fixed effects, including OLS, IV, and Poisson regression. The library also provides tools for robust inference, including heteroscedasticity-robust and cluster robust standard errors, as well as the wild cluster bootstrap. Additionally, PyFixest implements several routines for difference-in-differences estimation with staggered treatment adoption. PyFixest aims to faithfully replicate the core design principles of "fixest", offering post-estimation inference adjustments, user-friendly syntax for multiple estimations, and efficient post-processing capabilities. By making efficient use of jit-compilation, it is also one of the fastest solutions for regressions with high-dimensional fixed effects. The presentation will cover PyFixest's functionality, design philosophy, and future development prospects. https://www.youtube.com/watch?v=kSQxGGA7Rr4 https://www.youtube-nocookie.com/embed/kSQxGGA7Rr4 2024-06-23 yes https://pycon.de/archive/2024/talks/moving-from-offline-to-online-machine-learning-with-river/ 2024-06-23 https://pycon.de/static/media/social/talks/G9S3MR.png Moving from Offline to Online Machine Learning with River https://i.ytimg.com/vi/dLrVg6vf_1c/maxresdefault.jpg Moving from Offline to Online Machine Learning with River The foundations of machine learning were built on offline batch processing techniques for model training and inference. As organisations become more dependent on real-time data, the technological trend for machine learning in production is moving towards adding an online stream processing approach. This has benefits such as lower computational requirements due to being able to incrementally learn from a stream of data points, which enables the continual upgrading of models by adapting to real-time changes in data. Learn how to get started on your online ML journey with River https://www.youtube.com/watch?v=dLrVg6vf_1c https://www.youtube-nocookie.com/embed/dLrVg6vf_1c 2024-06-23 yes https://pycon.de/archive/2024/talks/next-stop-insights-how-streamlit-and-snowflake-power-up-data-stories/ 2024-06-23 https://pycon.de/static/media/social/talks/83ZGV3.png Next Stop: Insights! How Streamlit and Snowflake Power Up Data Stories https://i.ytimg.com/vi/Vinwx4w-iUU/maxresdefault.jpg Next Stop: Insights! How Streamlit and Snowflake Power Up Data Stories Data stories transform complex data insights into clear, actionable and context rich narratives to drive business value. The presentation of data stories to different audiences in a visually compelling manner while keeping track of data changes is a challenging task. A possible solution is to implement appealing and interactive data applications, for which Streamlit is an established open-source solution. In combination with Snowflake, it enables an efficient and straightforward approach to build engaging data applications that utilize data directly from a data platform. In this talk, we will explore a proof-of-concept, tracing the conception of a data story to the implementation of a Streamlit app in Snowflake by using open source datasets from Deutsche Bahn. So, hold onto your seats – it is time to explore the world of data apps with Snowflake and Streamlit. https://www.youtube.com/watch?v=Vinwx4w-iUU https://www.youtube-nocookie.com/embed/Vinwx4w-iUU 2024-06-23 yes https://pycon.de/archive/2024/talks/no-more-raw-sql-sqlalchemy-orms-asyncio/ 2024-10-28 https://pycon.de/static/media/social/talks/EHJRVF.png No More Raw SQL: SQLAlchemy, ORMs & asyncio https://i.ytimg.com/vi/b-N_6vVdEM0/maxresdefault.jpg No More Raw SQL: SQLAlchemy, ORMs & asyncio Managing a database and synchronizing service data representation with the database can be tricky. In this workshop, you’ll learn how to use SQLAlchemy, a powerful SQL toolkit, to simplify this task. We’ll cover how to leverage SQLAlchemy’s Object Relational Mapper (ORM) system, and how to use SQLAlchemy's asyncio extension in your async services. Participants will walk out of this tutorial having learned how to: - Use SQLAlchemy for database operations in Python, enhancing the readability and maintainability of the code - Build Python classes (ORMs) that represent the database tables - Experiment with different relationship-loading techniques to improve querying performance - Utilize SQLAlchemy’s asyncio extension to interact with databases asynchronously https://www.youtube.com/watch?v=b-N_6vVdEM0 https://www.youtube-nocookie.com/embed/b-N_6vVdEM0 2024-10-28 yes https://pycon.de/archive/2024/talks/pandas-dask-dataframe-2-0-comparison-to-spark-duckdb-and-polars/ 2024-06-23 https://pycon.de/static/media/social/talks/N9DEVW.png Pandas + Dask DataFrame 2.0 - Comparison to Spark, DuckDB and Polars https://i.ytimg.com/vi/qyvLJ2LvKLc/maxresdefault.jpg Pandas + Dask DataFrame 2.0 - Comparison to Spark, DuckDB and Polars Dask is a library for distributed computing with Python that integrates tightly with pandas. Historically, Dask was the easiest choice to use (it’s just pandas) but struggled to achieve robust performance (there were many ways to accidentally perform poorly). The re-implementation of the DataFrame API addresses all of the pain points that users ran into. We will look into how Dask is a lot faster now, how it performs on benchmarks that is struggled with in the past and how it compares to other tools like Spark, DuckDB and Polars. https://www.youtube.com/watch?v=qyvLJ2LvKLc https://www.youtube-nocookie.com/embed/qyvLJ2LvKLc 2024-06-23 yes https://pycon.de/archive/2024/talks/performant-scientific-computation-in-python-and-rust/ 2024-10-28 https://pycon.de/static/media/social/talks/XBUHCK.png Performant, scientific computation in Python and Rust https://i.ytimg.com/vi/wvjcMYTXVSU/maxresdefault.jpg Performant, scientific computation in Python and Rust A tutorial session on how to build scientific packages for numerical calculus and algorithms in Python and Rust. It walks through the process of packaging with a modern tool stack, introduces the concept of vectorization for efficient computation in Python in the context of classical Machine Learning, and shows how the package can be optimized with extensions written in Rust. https://www.youtube.com/watch?v=wvjcMYTXVSU https://www.youtube-nocookie.com/embed/wvjcMYTXVSU 2024-10-28 yes https://pycon.de/archive/2024/talks/personalizing-carousel-ranking-on-wolt-s-discovery-page-a-hierarchical-multi-armed-bandit-approach/ 2024-06-23 https://pycon.de/static/media/social/talks/7J7LEB.png Personalizing Carousel Ranking on Wolt's Discovery Page: A Hierarchical Multi-Armed Bandit Approach https://i.ytimg.com/vi/-zHASP0vWoM/maxresdefault.jpg Personalizing Carousel Ranking on Wolt's Discovery Page: A Hierarchical Multi-Armed Bandit Approach Wolt's Discovery page serves as the primary gateway for millions of weekly users exploring diverse cuisines and products. With over 130,000 merchants in 25 countries, presenting relevant content poses a unique challenge. In this presentation, we address the complexities of personalizing the Discovery page using a hierarchical multi-armed bandit (MAB) approach built on the Python ecosystem. We outline the challenges specific to an expansive online delivery platform, introducing our MAB solution that incorporates hierarchical parameters at user, segment, city, and country levels. Leveraging Thompson Sampling for exploration and exploitation, our approach accommodates data sparsity challenges. Evaluation results, both offline and online, showcase the effectiveness of our solution. The talk concludes with insights into the resilient, scalable, and adaptive architecture underpinning our approach, featuring open-source libraries such as mlflow, Flyte, and Seldon Core. Our learnings and future steps toward a personalized, context-aware Discovery page cap off the presentation. Join us as we navigate the intricacies of recommendation challenges in the dynamic world of quick commerce. https://www.youtube.com/watch?v=-zHASP0vWoM https://www.youtube-nocookie.com/embed/-zHASP0vWoM 2024-06-23 yes https://pycon.de/archive/2024/talks/polars-and-time-series-what-it-can-do-and-how-to-overcome-any-limitation/ 2024-06-23 https://pycon.de/static/media/social/talks/LNFSDV.png Polars and Time Series: what it can do, and how to overcome any limitation https://i.ytimg.com/vi/qz-zAHBz6Ks/maxresdefault.jpg Polars and Time Series: what it can do, and how to overcome any limitation Time series analysis is ubiquitous in applied data science because of the value it delivers. In order to do effective time series analysis, you need to know your tools well. Polars has excellent built-in time series support, and it's also possible to extend it where necessary. We will talk about: - Basic built-in time series operations with Polars (e.g. "what's the average number of sales per month?"). - numba/numpy/scipy interoperability for not-so-basic time series operations (e.g. non-linear interpolation, or cumulative operations). - Advanced, custom time series operations, and how you can implement them as Polars plugins (e.g. business day arithmetic). Basic interest and knowledge of Python and data will be assumed, but no prior Polars experience is required. Anyone working with time series and/or dataframes will likely benefit from the talk. https://www.youtube.com/watch?v=qz-zAHBz6Ks https://www.youtube-nocookie.com/embed/qz-zAHBz6Ks 2024-06-23 yes https://pycon.de/archive/2024/talks/power-structures-the-fair-advantage/ 2024-10-28 https://pycon.de/static/media/social/talks/8LNYPD.png Power structures. The fair advantage https://i.ytimg.com/vi/Eyr-QV9e3Ns/maxresdefault.jpg Power structures. The fair advantage Humans are complex. As developers, we wanna ignore that ... but to do our job right, we cannot. Let's talk about power, motivation, techno-sociology, politics and why all of this is important for our job. https://www.youtube.com/watch?v=Eyr-QV9e3Ns https://www.youtube-nocookie.com/embed/Eyr-QV9e3Ns 2024-10-28 yes https://pycon.de/archive/2024/talks/prescriptive-analytics-in-the-python-ecosystem-with-gurobi/ 2024-06-23 https://pycon.de/static/media/social/talks/KCYDM9.png Prescriptive Analytics in the Python Ecosystem with Gurobi https://i.ytimg.com/vi/-xXUVM2UJlg/maxresdefault.jpg Prescriptive Analytics in the Python Ecosystem with Gurobi Join us as we guide you through integrating Gurobi and prescriptive analytics into your greater Python ecosystem. We’ll demonstrate model-building patterns based on NumPy and SciPy.sparse data structures and explore how to take advantage of indexed DataFrames and Series in pandas for mathematical model building. You’ll also discover how to use trained regressors from scikit-learn as constraints in optimization models. Join us as we delve into the world of optimization with Gurobi and elevate your workflows. https://www.youtube.com/watch?v=-xXUVM2UJlg https://www.youtube-nocookie.com/embed/-xXUVM2UJlg 2024-06-23 yes https://pycon.de/archive/2024/talks/public-money-public-experiment-open-source-processes-in-the-public-administration/ 2024-10-28 https://pycon.de/static/media/social/talks/DEKGYM.png Public Money, Public Experiment - open source processes in the public administration https://i.ytimg.com/vi/aHrmI3Iz1cA/maxresdefault.jpg Public Money, Public Experiment - open source processes in the public administration Imagine a data lab in a federal ministry wants to publish python applications - how long could it possibly take? While open code is widely acknowledged as beneficial, the lack of thriving open code platforms from public institutions gets you wondering: a day, a week, months, or even years? When publishing code, a private person, a company or a public institution all face unique circumstances and take different considerations into account. While individuals or companies frequently publish their code and share their experiences, less is known about these processes in public institutions. In our talk we will cover how a data lab, located in a federal ministry would go about this topic. We will share insights into the publishing process, touching upon existing pioneers and the alignment of open source with administrative principles, as well as the hurdles, surprises, and regulatory considerations of our journey. Since we are a newly established unit with the word lab in our name, our talk delves into a unique real-world experiment: How much progress can our data lab make in publishing code within the three months leading up to PyCon DE & PyData Berlin 2024? https://www.youtube.com/watch?v=aHrmI3Iz1cA https://www.youtube-nocookie.com/embed/aHrmI3Iz1cA 2024-10-28 yes https://pycon.de/archive/2024/talks/put-your-rag-to-the-test-component-per-component-evaluation-of-our-llm-powered-airplane-manufacturing-assistant/ 2024-01-07 https://pycon.de/static/media/social/talks/WEVXJS.png Put your RAG to the test: Component-per-component evaluation of our LLM-powered airplane manufacturing assistant https://pycon.de/archive/2024/talks/pycon-community-backstage-a-decade-of-camaraderie-growth-and-lessons-learned/ 2024-10-28 https://pycon.de/static/media/social/talks/7EC3UY.png PyCon Community Backstage: A Decade of Camaraderie, Growth, and Lessons Learned https://i.ytimg.com/vi/ztMEsIhez9k/maxresdefault.jpg PyCon Community Backstage: A Decade of Camaraderie, Growth, and Lessons Learned For the past decade, my journey as a dedicated community organizer has allowed me to immerse myself deeply in the Python community, experiencing its extraordinary growth firsthand. The transition of Python from being a top-10 contender to becoming the foremost programming language has been an exhilarating experience, propelled by a burgeoning community and its foray into fields such as data science and artificial intelligence. The inclusivity and camaraderie within the Python community have been pivotal, illustrating how collective effort and a nurturing culture are instrumental to its current standing. This presentation is crafted to disseminate the pivotal lessons and best practices that have emerged from my decade-long engagement. During this period, I have played a key role in organizing over twenty Python/PyData conferences, including notable events like PyCon.DE, PyData Berlin, EuroPython, EuroSciPy, and PyData Global. It is for anyone who wants to learn more about, contribute to and organize themselves in the Python and PyData community. This talk will address: * How it works: community backstage * Why it works: community organizations * Lessons learned: * community leadership & team dynamics * balancing ideas and realities * personal & professional growth * How to contribute as an individual, community or company * How organizations like the [PySV](https://pysv.org), [NumFOCUS](https://numfocus.org) or PioneersHub serve the community https://www.youtube.com/watch?v=ztMEsIhez9k https://www.youtube-nocookie.com/embed/ztMEsIhez9k 2024-10-28 yes https://pycon.de/archive/2024/talks/pyladies-panel-reflecting-within-challenging-narratives-in-tech-feminism/ 2024-10-28 https://pycon.de/static/media/social/talks/BFYUUJ.png (PyLadies Panel) Reflecting Within: Challenging Narratives in Tech Feminism https://i.ytimg.com/vi/UrnHaUvUxwc/maxresdefault.jpg (PyLadies Panel) Reflecting Within: Challenging Narratives in Tech Feminism For the third year in a role, the PyLadies Panel at PyCon PyData engages with a broader audience on critical issues related to gender disparities, ethics, and the ongoing importance of women-focused tech groups. Adopting unconventional formats, the PyLadies Panel aims to foster meaningful discussions among PyLadies members and the Python community, encouraging open dialogue and community solidarity. https://www.youtube.com/watch?v=UrnHaUvUxwc https://www.youtube-nocookie.com/embed/UrnHaUvUxwc 2024-10-28 yes https://pycon.de/archive/2024/talks/pyo3-101-writing-python-modules-in-rust/ 2024-10-28 https://pycon.de/static/media/social/talks/8C83EA.png PyO3 101 - Writing Python modules in Rust https://i.ytimg.com/vi/FWkCPYl_58M/maxresdefault.jpg PyO3 101 - Writing Python modules in Rust In this interactive workshop, we will cover the very basics of using PyO3. There will be hands-on exercises to go from how to set up the project environment to writing a "toy" Python library written in Rust using PyO3. We will cover a lot of specifications of the API provided by PyO3 to create Python functions, modules, handling errors and converting types. https://www.youtube.com/watch?v=FWkCPYl_58M https://www.youtube-nocookie.com/embed/FWkCPYl_58M 2024-10-28 yes https://pycon.de/archive/2024/talks/pytest-tips-and-tricks-for-a-better-testsuite/ 2024-10-28 https://pycon.de/static/media/social/talks/DSFWRC.png pytest tips and tricks for a better testsuite https://i.ytimg.com/vi/FG_DgVo0hU0/maxresdefault.jpg pytest tips and tricks for a better testsuite pytest lets you write simple tests fast - but also scales to very complex scenarios: Beyond the basics of no-boilerplate test functions, this training will show various intermediate/advanced features, as well as gems and tricks. To attend this training, you should already be familiar with the pytest basics (e.g. writing test functions, parametrize, or what a fixture is) and want to learn how to take the next step to improve your test suites. If you're already familiar with things like fixture caching scopes, autouse, or using the built-in `tmp_path`/`monkeypatch`/... fixtures: There will probably be some slides about concepts you already know, but there are also various little hidden tricks and gems I'll be showing. https://www.youtube.com/watch?v=FG_DgVo0hU0 https://www.youtube-nocookie.com/embed/FG_DgVo0hU0 2024-10-28 yes https://pycon.de/archive/2024/talks/python-3-12-s-new-monitoring-and-debugging-api/ 2024-10-28 https://pycon.de/static/media/social/talks/P7AG9A.png Python 3.12's new monitoring and debugging API https://i.ytimg.com/vi/i7CbCNGfMvI/maxresdefault.jpg Python 3.12's new monitoring and debugging API Python 3.12 introduced a new low-impact monitoring API with [PEP669](https://peps.python.org/pep-0669/), which can be used to implement far faster debuggers than ever before. This talk covers the main advantages of this API and how you can use it to develop small tools. https://www.youtube.com/watch?v=i7CbCNGfMvI https://www.youtube-nocookie.com/embed/i7CbCNGfMvI 2024-10-28 yes https://pycon.de/archive/2024/talks/python-monorepos-the-polylith-developer-experience/ 2024-10-28 https://pycon.de/static/media/social/talks/VEACZM.png Python Monorepos: The Polylith Developer Experience https://i.ytimg.com/vi/wGWjt9GJLU4/maxresdefault.jpg Python Monorepos: The Polylith Developer Experience What if writing software could be more like building with LEGO bricks? A more playful and productive developer experience. For me, that is all about writing code without the hassle. A productive setup should also let let us make design decisions while learning what to actually build, and allow changes during the way. Polylith solves this in a nice and simple way. I am the developer of the Open Source Python-specific tooling for Polylith. I’ll walk through the simple Architecture & the Developer friendly tooling for a joyful Python Experience. https://www.youtube.com/watch?v=wGWjt9GJLU4 https://www.youtube-nocookie.com/embed/wGWjt9GJLU4 2024-10-28 yes https://pycon.de/archive/2024/talks/rag-for-a-medical-company-the-technical-and-product-challenges/ 2024-06-23 https://pycon.de/static/media/social/talks/XMKREA.png RAG for a medical company: the technical and product challenges https://i.ytimg.com/vi/InyinN_hHhA/maxresdefault.jpg RAG for a medical company: the technical and product challenges [RAG (Retrieval Augmented Generation)](https://www.pinecone.io/learn/retrieval-augmented-generation/) is the process of querying a (large) set of documents with natural language, leveraging vector search and llms. While it has recently become widely accessible to develop a Proof-Of-Concept RAG using OpenAI and one of the various open-source contributions (e.g. langchain), building a **performant** RAG that **brings value to users** is challenging. This talk will focus on learnings from building a RAG for a **medical company**, to allow doctors to query drug documentation with natural language, using tools like **[Chainlit](https://docs.chainlit.io/get-started/overview), [Qdrant](https://qdrant.tech/) and [Langsmith](https://www.langchain.com/langsmith)**. Naturally, a product question emerged: how to effectively leverage LLMs that **can never guarantee 100% accuracy** in the health sector? We will explain how we addressed this challenge, as well as the various **technical improvements** implemented to enhance both the retrieval (vector search) and generation (llm) metrics of our RAG. https://www.youtube.com/watch?v=InyinN_hHhA https://www.youtube-nocookie.com/embed/InyinN_hHhA 2024-06-23 yes https://pycon.de/archive/2024/talks/refactoring-large-programs/ 2024-10-28 https://pycon.de/static/media/social/talks/CMM8S3.png Refactoring Large Programs https://i.ytimg.com/vi/gwvgVG8DH6g/maxresdefault.jpg Refactoring Large Programs One of the most challenging tasks in software engineering is cleaning up a complex software with 10,000-100,000 lines of code. The problem gets worse, if you are taking over legacy code. The fact that the Python language does neither enforce strict typing or encapsulation does not help either. What should you do if throwing away everything and rewriting the program from scratch is not an option? In this tutorial, we will exercise refactoring a larger program that is undocumented, unstructured and untested. We will take a messy example program and work through a list of procedures that may help you in your next big refactoring. https://www.youtube.com/watch?v=gwvgVG8DH6g https://www.youtube-nocookie.com/embed/gwvgVG8DH6g 2024-10-28 yes https://pycon.de/archive/2024/talks/reinforcement-learning-bridging-the-gap-between-research-and-applications/ 2024-06-23 https://pycon.de/static/media/social/talks/SSKV9R.png Reinforcement Learning: Bridging The Gap Between Research and Applications https://i.ytimg.com/vi/k45E30KhBpk/maxresdefault.jpg Reinforcement Learning: Bridging The Gap Between Research and Applications Reinforcement learning (RL) has great potential for industrial applications, but few mature software frameworks exist to facilitate its use. This talk discusses efforts to improve the software landscape for RL, making it easier for researchers to contribute algorithms and for engineers to apply RL in real-world settings. Specifically, we highlight the open-source library Tianshou, which provides high-level interfaces for painless RL application development along with lower-level APIs that cater to the needs of researchers. By improving RL software, we aim to accelerate research progress and expand RL adoption in industry. https://www.youtube.com/watch?v=k45E30KhBpk https://www.youtube-nocookie.com/embed/k45E30KhBpk 2024-06-23 yes https://pycon.de/archive/2024/talks/replacing-callbacks-with-generators-a-case-study-in-computer-assisted-live-music/ 2024-10-28 https://pycon.de/static/media/social/talks/Y7R9GZ.png Replacing Callbacks with Generators: A Case Study in Computer-Assisted Live Music https://i.ytimg.com/vi/PkAE6dsqIJw/maxresdefault.jpg Replacing Callbacks with Generators: A Case Study in Computer-Assisted Live Music *Callbacks* have become an ubiquitous programming technique that we use every day without even thinking about it. They are definitely handy in many situations, but sometimes they feel more like a burden than a help. In developing an interactive realtime audio processing system for use on stage in live music, we encountered such a situation. This talk will present how a few dozen lines adding a thin abstraction layer allowed us to replace a complex callback mess with tremendously more readable *generators* (yes, you know, those functions which `yield` results instead of `return`ing them...). https://www.youtube.com/watch?v=PkAE6dsqIJw https://www.youtube-nocookie.com/embed/PkAE6dsqIJw 2024-10-28 yes https://pycon.de/archive/2024/talks/robust-configuration-management-with-pydantic-s-data-validation/ 2024-10-28 https://pycon.de/static/media/social/talks/RGWDCN.png Robust Configuration Management with Pydantic's Data Validation https://i.ytimg.com/vi/ZvcZDxS_mYE/maxresdefault.jpg Robust Configuration Management with Pydantic's Data Validation As applications grow, so do the amount of configurable features. Managing consistent defaults, maintaining user and developer documentation, and ensuring uniform parsing among a growing number of client applications can become a challenge. Adding constraints like complex fallback hierarchies and backwards compatibility, increases the probability of runtime errors. We show how [`Pydantic`'s](https://pydantic.dev/) strong data validation and integration into Python's type annotations can help building a strict specification for your configuration format, catch misconfiguration early, and mitigate the aforementioned problems with a non-formalized configuration management system. https://www.youtube.com/watch?v=ZvcZDxS_mYE https://www.youtube-nocookie.com/embed/ZvcZDxS_mYE 2024-10-28 yes https://pycon.de/archive/2024/talks/safeguarding-privacy-and-mitigating-vulnerabilities-navigating-security-challenges-in-generative-ai/ 2024-06-23 https://pycon.de/static/media/social/talks/MTVWQM.png Safeguarding Privacy and Mitigating Vulnerabilities: Navigating Security Challenges in Generative AI https://i.ytimg.com/vi/jiPAbcZ2ftc/maxresdefault.jpg Safeguarding Privacy and Mitigating Vulnerabilities: Navigating Security Challenges in Generative AI Generative AI (GenAI) has significantly improved our daily lives, prompting a focus on its integration into products and our routines. However, the growing importance of GenAI brings along significant concerns regarding privacy and vulnerability. This talk delves into the critical issues surrounding the protection of private data and the security of GenAI systems. We'll begin by understanding the fundamental differences between data privacy and data security. Drawing insights from real-life data breaches and compromised information in major companies, we'll explore the mistakes made and the steps taken to rectify them. Throughout the discussion, we'll analyze the challenges faced by GenAI in ensuring data privacy and security across various stages of an LLM project. Furthermore, the talk will shed light on how prominent companies building GenAI are working to reduce the impact of data privacy and security concerns within their models. Additionally, we'll explore strategies for individuals, like ourselves, using GenAI, to enhance data privacy and security when integrating it into our products or daily lives. Finally, the role and significance of government regulations in ensuring the safety and security of GenAI will be emphasized. https://www.youtube.com/watch?v=jiPAbcZ2ftc https://www.youtube-nocookie.com/embed/jiPAbcZ2ftc 2024-06-23 yes https://pycon.de/archive/2024/talks/securing-python-race-condition-vulnerabilities/ 2024-10-28 https://pycon.de/static/media/social/talks/A8HJHV.png Securing Python: Race Condition Vulnerabilities https://i.ytimg.com/vi/wzpFKWTa0kM/maxresdefault.jpg Securing Python: Race Condition Vulnerabilities This workshop addresses the critical and often underestimated topic of race conditions in Python, with a focus on their security implications. We begin with an overview of race conditions, explaining their nature and the security risks they pose. Participants will engage with small Python applications designed to demonstrate these vulnerabilities. Through hands-on analysis, we identify where and why these race conditions occur. The session progresses to simulate attacks exploiting these weaknesses, highlighting their potential for exploitation. Finally, we explore effective mitigation strategies, emphasizing thread synchronization and safe programming practices. The workshop aims to equip attendees with a deep understanding of race conditions in Python and practical skills to enhance the security and robustness of their code. https://www.youtube.com/watch?v=wzpFKWTa0kM https://www.youtube-nocookie.com/embed/wzpFKWTa0kM 2024-10-28 yes https://pycon.de/archive/2024/talks/select-ml-from-databases/ 2024-06-23 https://pycon.de/static/media/social/talks/RBNJRK.png Select ML from Databases https://i.ytimg.com/vi/Gh9UZ95GBNg/maxresdefault.jpg Select ML from Databases This talk introduces a new workflow for building your machine learning models using the capabilities of modern databases that support machine learning use cases natively. There is an overview of how machine learning models are being created today to how they could look in the near future by utilising the features provided by current databases. https://www.youtube.com/watch?v=Gh9UZ95GBNg https://www.youtube-nocookie.com/embed/Gh9UZ95GBNg 2024-06-23 yes https://pycon.de/archive/2024/talks/streamlining-python-development-a-guide-to-a-modern-project-setup/ 2024-10-28 https://pycon.de/static/media/social/talks/CBVTEG.png Streamlining Python Development: A Guide to a Modern Project Setup https://i.ytimg.com/vi/Qwu11p41YF0/maxresdefault.jpg Streamlining Python Development: A Guide to a Modern Project Setup Designed for beginners, this presentation demystifies Python project management using [Hatch](https://hatch.pypa.io/) and delves into `pyproject.toml` for efficient configuration. We'll guide you through organizing directories, implementing unit testing for code reliability, and using [mypy](https://mypy-lang.org/) for type checking to enhance code quality. The session concludes with insights into [ruff](https://github.com/astral-sh/ruff), a modern linter for maintaining Python standards, which is replacing black, isort, flake8. This talk is a comprehensive toolkit for anyone eager to learn and apply the latest practices in Python development. https://www.youtube.com/watch?v=Qwu11p41YF0 https://www.youtube-nocookie.com/embed/Qwu11p41YF0 2024-10-28 yes https://pycon.de/archive/2024/talks/streamlining-python-development-a-practical-approach-to-ci-cd-with-github-actions/ 2024-10-28 https://pycon.de/static/media/social/talks/YHMUCL.png Streamlining Python Development: A Practical Approach to CI/CD with GitHub Actions https://i.ytimg.com/vi/eNi8ALNKc54/maxresdefault.jpg Streamlining Python Development: A Practical Approach to CI/CD with GitHub Actions Crafting code for minimal dependencies and maximum portability is an art. This talk focuses on how continuous integration and delivery ensure project resilience to Python updates and changes in the packaging ecosystem. Setting up automation around your project enhances peace of mind, improves code maintainability, and facilitates collaboration. https://www.youtube.com/watch?v=eNi8ALNKc54 https://www.youtube-nocookie.com/embed/eNi8ALNKc54 2024-10-28 yes https://pycon.de/archive/2024/talks/tackling-the-cold-start-challenge-in-demand-forecasting/ 2024-06-23 https://pycon.de/static/media/social/talks/H3X3AX.png Tackling the Cold Start Challenge in Demand Forecasting https://i.ytimg.com/vi/dm3lDANtp-0/maxresdefault.jpg Tackling the Cold Start Challenge in Demand Forecasting In this talk, we address the Cold Start problem in Demand Forecasting, focusing on scenarios where historical data is scarce or nonexistent. This constitutes a common situation in practice, such as with the launch of new products in Retail. However, many Time Series and Machine Learning models encounter difficulties in handling this challenge, primarily due to their dependence on a substantial amount of historical data for effective training and prediction. We begin by providing an overview of established techniques used to address the Cold Start problem, including methods like padding, feature engineering, and leveraging item similarities. Additionally, we explore more recent advancements and emerging research, such as Transfer Learning for Time Series. While each technique presents its unique set of trade-offs, the challenge lies in determining the most suitable approach for a given dataset or use case. This aspect is often not widely understood, and our goal is to unravel this complexity by offering practical insights. Furthermore, we introduce a practical framework for systematically evaluating different forecasting strategies within the Cold Start setting, guiding you in selecting the most suitable approach for your datasets and use cases. https://www.youtube.com/watch?v=dm3lDANtp-0 https://www.youtube-nocookie.com/embed/dm3lDANtp-0 2024-06-23 yes https://pycon.de/archive/2024/talks/tailored-and-trending-key-learnings-from-3-years-of-news-recommendations/ 2023-12-15 https://pycon.de/static/media/social/talks/XDQNCR.png Tailored and Trending: Key learnings from 3 years of news recommendations https://pycon.de/archive/2024/talks/thats-it-dealing-with-unexpected-data-problems/ 2024-06-23 https://pycon.de/static/media/social/talks/JKWBBR.png That’s it?! Dealing with unexpected data problems https://i.ytimg.com/vi/R7oLrkUrHYM/maxresdefault.jpg That’s it?! Dealing with unexpected data problems Drawing on experience with multiple consulting projects, this talk shares experiences on how to deal with unexpected data problems. We are discussing how fare purely technical solutions as well as domain knowledge can be deployed to compensate for lacking data quality or quantity and when it might be better to scale down the original project scope. https://www.youtube.com/watch?v=R7oLrkUrHYM https://www.youtube-nocookie.com/embed/R7oLrkUrHYM 2024-06-23 yes https://pycon.de/archive/2024/talks/the-ai-revolution-will-not-be-monopolized-how-open-source-beats-economies-of-scale-even-for-llms/ 2024-06-23 https://pycon.de/static/media/social/talks/QYPLJE.png The AI Revolution Will Not Be Monopolized: How open-source beats economies of scale, even for LLMs https://i.ytimg.com/vi/GHKGEnDSVrY/maxresdefault.jpg The AI Revolution Will Not Be Monopolized: How open-source beats economies of scale, even for LLMs With the latest advancements in Natural Language Processing and Large Language Models (LLMs), and big companies like OpenAI dominating the space, many people wonder: Are we heading further into a black box era with larger and larger models, obscured behind APIs controlled by big tech monopolies? I don’t think so, and in this talk, I’ll show you why. I’ll dive deeper into the open-source model ecosystem, some common misconceptions about use cases for LLMs in industry, practical real-world examples and how basic principles of software development such as modularity, testability and flexibility still apply. LLMs are a great new tool in our toolkits, but the end goal remains to create a system that does what you want it to do. Explicit is still better than implicit, and composable building blocks still beat huge black boxes. https://www.youtube.com/watch?v=GHKGEnDSVrY https://www.youtube-nocookie.com/embed/GHKGEnDSVrY 2024-06-23 yes https://pycon.de/archive/2024/talks/the-evolution-of-feature-stores/ 2024-06-23 https://pycon.de/static/media/social/talks/BNCJPV.png The evolution of Feature Stores https://i.ytimg.com/vi/48bx6f1qtYM/maxresdefault.jpg The evolution of Feature Stores Feature Stores have become an important component of the machine learning lifecycle. They have been particularly pivotal in bridging the gap between data engineering and machine learning workflows(experimentation, training and serving). This talk will explore Feature Stores with a focus on their evolution, what they look like now and what they could look like in the future with the advent of the AI ACT. https://www.youtube.com/watch?v=48bx6f1qtYM https://www.youtube-nocookie.com/embed/48bx6f1qtYM 2024-06-23 yes https://pycon.de/archive/2024/talks/the-key-to-reliability-testing-in-the-field-of-ml-ops/ 2024-10-28 https://pycon.de/static/media/social/talks/9PZSBS.png The key to reliability - Testing in the field of ML-Ops https://i.ytimg.com/vi/1bUZXfB06z0/maxresdefault.jpg The key to reliability - Testing in the field of ML-Ops Testing is a de facto standard in modern software development. With increasing awareness that comes with ML-Ops, testing becomes more important for the development and operation of machine learning-based components. In this talk we would like to share our view and solution for testing in the field of machine learning. We will present the applied testing strategy used and the lessons learned from the last four years of experience in operating idealo’s cataloging system. https://www.youtube.com/watch?v=1bUZXfB06z0 https://www.youtube-nocookie.com/embed/1bUZXfB06z0 2024-10-28 yes https://pycon.de/archive/2024/talks/the-pragmatic-pythonic-data-engineer/ 2024-06-23 https://pycon.de/static/media/social/talks/NYFVLM.png The pragmatic Pythonic data engineer https://i.ytimg.com/vi/tSohi4-Pu88/maxresdefault.jpg The pragmatic Pythonic data engineer Learn to make practical decisions in data engineering with Python's vast ecosystem. Avoid blindly following market guidelines and consider the reality of your situation for better performance and architecture. https://www.youtube.com/watch?v=tSohi4-Pu88 https://www.youtube-nocookie.com/embed/tSohi4-Pu88 2024-06-23 yes https://pycon.de/archive/2024/talks/the-secret-life-of-metaclasses/ 2024-10-28 https://pycon.de/static/media/social/talks/BKBNRF.png The Secret Life of Metaclasses https://i.ytimg.com/vi/Wdzo8Xyml6M/maxresdefault.jpg The Secret Life of Metaclasses Metaclasses. What are they? Where do they live? How do they reproduce? Did you know that you can make your classes receive keyword arguments, just like functions? And that they can be decorated as well? Do you want to understand how classes, metaclasses and decorators work and what are they good for? In this hands-on coding session we will inspect the inner workings of how Python creates classes, and how decorators, meta-classes and methods from superclasses can influence this process. We'll explore: * normal and special methods * how attribute lookup works between instances and classes * what are descriptors, and how they fit into attribute lookup process * what is the relationship between instances, classes and metaclasses * what are metaclasses for * and some other metaprogramming odds and ends All that is required for you to enjoy this session is that you have written a class in Python. If you've done the original [Python Tutorial](https://docs.python.org/3/tutorial/index.html), that should be more than enough. https://www.youtube.com/watch?v=Wdzo8Xyml6M https://www.youtube-nocookie.com/embed/Wdzo8Xyml6M 2024-10-28 yes https://pycon.de/archive/2024/talks/the-struggles-we-skipped-data-engineering-for-the-tiktok-generation/ 2024-06-23 https://pycon.de/static/media/social/talks/DWGV7W.png The Struggles We Skipped: Data Engineering for the TikTok Generation https://i.ytimg.com/vi/JSn7gqVnvbY/maxresdefault.jpg The Struggles We Skipped: Data Engineering for the TikTok Generation In a world increasingly embracing Python, plug-and-play solutions and AI-generated code, our generation growing up with these advancements may not fully grasp the challenges faced by our predecessors. Meanwhile, data engineering, traditionally known for its complexity, can now transition into the plug-and-play realm too, thanks to Python libraries such as dlt. Aimed to be both fun and insightful, this talk will educate the listener on the concepts of data engineering our generation finds most important and enable them to use high level abstractions to automate most of what used to be highly manual work. The juniors will gain an appreciation for the difficulties in data pipeline engineering, the seniors - a straightforward solution to expedite the creation of robust pipelines. From the perspective of junior data engineers such as us, the talk will walk through the challenges associated with constructing a data pipeline and demonstrate how these can be effectively addressed using Python libraries such as dlt that simplify the intricacies of data extraction, transformation, and loading. https://www.youtube.com/watch?v=JSn7gqVnvbY https://www.youtube-nocookie.com/embed/JSn7gqVnvbY 2024-06-23 yes https://pycon.de/archive/2024/talks/the-taller-the-tree-the-harder-the-fall-determining-tree-height-from-space-using-deep-learning-and-very-high-resolution-satellite-imagery/ 2024-06-23 https://pycon.de/static/media/social/talks/ZFXZHG.png 🌳 The taller the tree, the harder the fall. Determining tree height from space using Deep Learning and very high resolution satellite imagery 🛰️ https://i.ytimg.com/vi/7bWGLmUbrYI/maxresdefault.jpg 🌳 The taller the tree, the harder the fall. Determining tree height from space using Deep Learning and very high resolution satellite imagery 🛰️ A case study of how we use Deep Learning based photogrammetry to calculate the height of trees from very high resolution satellite imagery. We show the substantial improvement achieved by switching from classical photogrammetric techniques to a deep learning based model (implemented in PyTorch), and the challenges we had to overcome to make this solution work. https://www.youtube.com/watch?v=7bWGLmUbrYI https://www.youtube-nocookie.com/embed/7bWGLmUbrYI 2024-06-23 yes https://pycon.de/archive/2024/talks/there-is-a-better-way-to-automate-and-manage-your-fluid-simulations/ 2024-06-23 https://pycon.de/static/media/social/talks/ML99UB.png There is a Better Way to Automate and Manage Your (Fluid) Simulations https://i.ytimg.com/vi/NGQlSScH97s/maxresdefault.jpg There is a Better Way to Automate and Manage Your (Fluid) Simulations This is a story about applying Python and the “hacker mindset” to Computer Aided Engineering (CAE), an emerging domain within the Python ecosystem. Shell scripts have traditionally been the preferred tool for automating CAE pipelines, especially in subfield of Computational Fluid Dynamics (CFD). However, this approach is brittle, severely limited and cumbersome to manage at scale. Data management is also a challenge, with tens to hundreds of GB per simulation needing to be stored and versioned in complex folder structures. One possible approach is to use Python as an automation and glue language and Data Version Control (DVC) which is a Python based tool built on top of git to track pipelines and data. https://www.youtube.com/watch?v=NGQlSScH97s https://www.youtube-nocookie.com/embed/NGQlSScH97s 2024-06-23 yes https://pycon.de/archive/2024/talks/time-series-anomaly-detection-with-a-human-in-the-loop/ 2024-06-23 https://pycon.de/static/media/social/talks/CMMJPN.png Time series anomaly detection with a human-in-the-loop https://i.ytimg.com/vi/OsdDCdQYgEE/maxresdefault.jpg Time series anomaly detection with a human-in-the-loop In the cross-industry wide trend towards industry 4.0 solutions, the amount of gathered sensor data is ever growing. Through the sheer amount of data, manual or human-based monitoring of the collected time series data becomes cumbersome if not even impossible. Yet, careful inspection of the time series data and identification of possible anomalies therein is crucial to detect problems in the underlying processes. To resolve this demand, ZEISS is developing a fully automated time series processing tool that performs ML based time series anomaly detection with a human-in-the-loop. https://www.youtube.com/watch?v=OsdDCdQYgEE https://www.youtube-nocookie.com/embed/OsdDCdQYgEE 2024-06-23 yes https://pycon.de/archive/2024/talks/un-leashed-potential-of-ai-in-government/ 2024-01-07 https://pycon.de/static/media/social/talks/Y3Y78W.png (Un)leashed potential of AI in Government https://pycon.de/archive/2024/talks/unleashing-confidence-in-sql-development-through-unit-testing/ 2024-10-28 https://pycon.de/static/media/social/talks/EMZ7L7.png Unleashing Confidence in SQL Development through Unit Testing https://i.ytimg.com/vi/ykNv7FP1494/maxresdefault.jpg Unleashing Confidence in SQL Development through Unit Testing As the landscape of data-driven applications expands, the need for robust SQL development practices becomes increasingly critical. This conference talk addresses the challenges faced by data teams in maintaining and evolving complex SQL models for their Data Warehouses, and shows how unit testing can play a vital role in ensuring data quality. We will delve into the significance of SQL unit testing, highlighting its ability to quickly validate modeling logic and making sure that modifications do not break existing behavior. With the ease of mind of an automatically verified SQL logic, changes to existing data models can be shipped with confidence, ultimately contributing to faster deployment cycles. Get detailed insights on the structure and functionality of Lotum’s SQL unit testing framework, built in Python using pytest and tailored for BigQuery. With Lotum processing millions of events from mobile games every day, explore how this robust framework allows for efficient testing, ensuring the accuracy of the SQL logic. Learn how test cases with small sets of static mock data can be defined effortlessly so that they help pinpoint potential code errors easily. https://www.youtube.com/watch?v=ykNv7FP1494 https://www.youtube-nocookie.com/embed/ykNv7FP1494 2024-10-28 yes https://pycon.de/archive/2024/talks/unlock-the-power-of-dev-containers-build-a-consistent-python-development-environment-in-seconds/ 2024-10-28 https://pycon.de/static/media/social/talks/UG8THG.png Unlock the Power of Dev Containers: Build a Consistent Python Development Environment in Seconds! https://i.ytimg.com/vi/Uhf9JDsKao0/maxresdefault.jpg Unlock the Power of Dev Containers: Build a Consistent Python Development Environment in Seconds! In this talk, we will explore the basic concepts of Dev Containers and demonstrate how they can support your everyday development as a Python programmer, data scientist, or machine learning engineer. With Dev Containers, you can build a consistent development environment in seconds, no matter where you are or what tools you use. And you know what? The Development Container Specification is even open source. Say goodbye to the hassle of setting up your development environment from scratch every time you start a new project! We will start with a basic example and discuss how to set up a consistent Python development environment, including best practices for package management and GPU support. After this talk, you will be able to leverage the advantages of Dev Containers, allowing you to work from anywhere and be ready in seconds. If you're tired of wasting time setting up your development environment and want to unlock the power of Dev Containers, then this talk is a must-attend for you! https://www.youtube.com/watch?v=Uhf9JDsKao0 https://www.youtube-nocookie.com/embed/Uhf9JDsKao0 2024-10-28 yes https://pycon.de/archive/2024/talks/using-llms-to-create-knowledge-graphs-from-a-large-corpus-of-parliamentary-debates/ 2024-06-23 https://pycon.de/static/media/social/talks/LWWQ9U.png Using LLMs to Create Knowledge Graphs From a Large Corpus of Parliamentary Debates https://i.ytimg.com/vi/vK5c32swkVI/maxresdefault.jpg Using LLMs to Create Knowledge Graphs From a Large Corpus of Parliamentary Debates Large Language Models (LLMs) have proven to be incredibly powerful on a range of tasks. They do however, have certain limitations when the input context becomes significantly large. Solutions such as Retrieval Augmented Generation (RAG) do a great job in providing context from custom data without retraining any models but they too have limitations, especially when the context is spread out over many documents. Consider the question “Which projects has person X worked on?”. Information required to answer this question may be spread out over hundreds of documents, making it difficult for an LLM alone to answer. One way to overcome this issue is to use an LLM as an entity extraction tool, which can extract entities and relationships from documents and load that data into a structured format such as a knowledge graph. In this talk, I will demonstrate this process on a dataset of parliamentary debates, showing how downstream analytics becomes more intuitive and feasible. https://www.youtube.com/watch?v=vK5c32swkVI https://www.youtube-nocookie.com/embed/vK5c32swkVI 2024-06-23 yes https://pycon.de/archive/2024/talks/using-ml-to-find-out-the-why-a-tutorial-in-causal-machine-learning/ 2023-12-21 https://pycon.de/static/media/social/talks/RMZLKZ.png Using ML to find out the "Why"? A Tutorial in Causal Machine Learning https://pycon.de/archive/2024/talks/when-and-how-to-start-coding-with-kids/ 2024-10-28 https://pycon.de/static/media/social/talks/UBNVYW.png When and how to start coding with kids https://i.ytimg.com/vi/DaaD1QDVmt8/maxresdefault.jpg When and how to start coding with kids Our world is driven by technology and there are many reasons to teach our kids how to code. For example, coding allows them to develop logical reasoning skills and teaches attention to detail. Allowing children to discover how much fun coding can be supports them in their development and opens many doors for their future. But when and how should we start coding with kids? This talk will approach the question from a scientific perspective, looking into how children's brains develop, how children learn and how to best teach them coding abilities. It will answer important questions like "At what age can a child start coding?" or "What are the benefits of learning to code?". It will also present possible starting points, like learning platforms or tutorials. https://www.youtube.com/watch?v=DaaD1QDVmt8 https://www.youtube-nocookie.com/embed/DaaD1QDVmt8 2024-10-28 yes https://pycon.de/archive/2024/talks/which-kind-of-software-tests-do-i-really-need/ 2024-10-28 https://pycon.de/static/media/social/talks/PVLTD3.png Which kind of software tests do I really need? https://i.ytimg.com/vi/x4Ki5-0syUg/maxresdefault.jpg Which kind of software tests do I really need? Explore a variety of software testing methodologies, from Manual and A/B Testing to Unit and Performance Tests. Learn how to make informed decisions for enhanced software delivery, matching the unique needs of your projects. https://www.youtube.com/watch?v=x4Ki5-0syUg https://www.youtube-nocookie.com/embed/x4Ki5-0syUg 2024-10-28 yes https://pycon.de/archive/2024/talks/whispered-secrets-building-an-open-source-tool-to-live-transcribe-summarize-conversations/ 2024-06-23 https://pycon.de/static/media/social/talks/7898PU.png Whispered Secrets: Building An Open-Source Tool To Live Transcribe & Summarize Conversations https://i.ytimg.com/vi/C8gm8S90Ys4/maxresdefault.jpg Whispered Secrets: Building An Open-Source Tool To Live Transcribe & Summarize Conversations Are you secretly a spy and/or passionate about open-source? Maybe you don't trust a cloud-hosted service with your highly classified information, or perhaps you like to build things for yourself. In this light-hearted talk, you will learn how to make a real-time on-device GenAI-powered application that can live transcribe and summarize conversations without internet access, using open-source components. Our journey begins with an introduction to open-source LLMs and the latest trends in running GenAI tools on your own hardware. We will build up our application step-by-step, first creating a live streaming voice-to-text transcription pipeline, then an LLM-based conversation summarization layer, presented within a Streamlit frontend, with conversation summaries sent to a lightweight Django API backend for storage. This talk is tailored for Python enthusiasts and requires no ML expertise. By seeing a practical demo come together piece by piece, attendees will gain a deeper understanding of how to build their own complex Generative AI applications and be pushed to imagine what they could make for themselves using on-device computation in real-world scenarios. https://www.youtube.com/watch?v=C8gm8S90Ys4 https://www.youtube-nocookie.com/embed/C8gm8S90Ys4 2024-06-23 yes https://pycon.de/archive/2024/talks/would-you-rely-on-chatgpt-to-dial-911-a-talk-on-balancing-determinism-and-probabilism-in-production-machine-learning-systems/ 2024-06-23 https://pycon.de/static/media/social/talks/YPKKQF.png Would you rely on ChatGPT to dial 911? A talk on balancing determinism and probabilism in production machine learning systems https://i.ytimg.com/vi/UIFwRXYKHgw/maxresdefault.jpg Would you rely on ChatGPT to dial 911? A talk on balancing determinism and probabilism in production machine learning systems In the last year there hasn’t been a day that passed without us hearing about a new generative AI innovation that will enhance some aspect of our lives. On a number of tasks large probabilistic systems are now outperforming humans, or at least they do so “on average”. “On average” means most of the time, but in many real life scenarios “average” performance is not enough: we need correctness ALL of the time, for example when you ask the system to dial 911. In this talk we will explore the synergy between deterministic and probabilistic models to enhance the robustness and controllability of machine learning systems. Tailored for ML engineers, data scientists, and researchers, the presentation delves into the necessity of using both deterministic algorithms and probabilistic model types across various ML systems, from straightforward classification to advanced Generative AI models. You will learn about the unique advantages each paradigm offers and gain insights into how to most effectively combine them for optimal performance in real-world applications. I will walk you through my past and current experiences in working with simple and complex NLP models, and show you what kind of pitfalls, shortcuts, and tricks are possible to deliver models that are both competent and reliable. The session will be structured into a brief introduction to both model types, followed by case studies in classification and generative AI, concluding with a Q&A segment. https://www.youtube.com/watch?v=UIFwRXYKHgw https://www.youtube-nocookie.com/embed/UIFwRXYKHgw 2024-06-23 yes https://pycon.de/archive/2024/talks/you-shall-not-pass-strengthen-your-python-code-against-attacks/ 2024-10-28 https://pycon.de/static/media/social/talks/7LQEJ3.png You shall not pass! 🧙 Strengthen your python code against attacks. https://i.ytimg.com/vi/2DuJMUmEx-U/maxresdefault.jpg You shall not pass! 🧙 Strengthen your python code against attacks. Have you ever thought about IT Security when coding your Python application? If not, you are not alone – but also not safe. Just recently, a research study counted almost 4000 secrets published on PyPI. Most of the secrets such as AWS Keys, Google API Keys or database credentials were most likely leaked accidentally. Leaked credentials top the list of entry points for attackers into protected areas. In this talk you’ll gain insights into how malicious attacks on Python applications are performed – and most importantly, how to protect yourself against them. We’ll kick off with a basic review of how to crack a password not only with brute force and continue with the most important IT Security principles. After understanding the importance of adhering to common security precautions, we will dive into Python coding hygiene. Where do the most common vulnerabilities lie? How can we strengthen the security of our code? We’ll cover secure coding practices such as code analysis, input validation and dependency vulnerabilities in theory and practice. Lastly, we will look at some case studies of common attacks on Python code and how to protect yourself against them. If you have never thought about security aspects in Python, this talk is for you! https://www.youtube.com/watch?v=2DuJMUmEx-U https://www.youtube-nocookie.com/embed/2DuJMUmEx-U 2024-10-28 yes https://pycon.de/archive/2024/talks/your-model-probably-memorized-the-training-data/ 2024-06-23 https://pycon.de/static/media/social/talks/BFF9VA.png Your Model _Probably_ Memorized the Training Data https://i.ytimg.com/vi/LhtYoASCEoM/maxresdefault.jpg Your Model _Probably_ Memorized the Training Data I know you probably don't want to hear about it, but your deep learning model probably memorized some of its training data. In this talk, we'll review active research on deep learning and memorization, particularly for large models such as large language and multi-modal models. We'll also explore potential ways to think through when this memorization is actually desired (and why) as well as threat vectors and legal risk of using models who have memorized training data. We'll also look at potential privacy protections which could address some of the issues and how to embrace memorization by thinking through different types of models and their use. https://www.youtube.com/watch?v=LhtYoASCEoM https://www.youtube-nocookie.com/embed/LhtYoASCEoM 2024-06-23 yes https://pycon.de/archive/2025/talks/3-ways-to-speed-up-your-regression-modeling-in-python/ 2025-01-05 https://pycon.de/static/media/social/talks/ECNDQM.png 3 Ways to Speed up Your Regression Modeling in Python https://i.ytimg.com/vi/T4GTPqy0vjo/maxresdefault.jpg 3 Ways to Speed up Your Regression Modeling in Python Linear Regression is the workhorse of statistics and data science. Some data scientists even go as far and argue that "linear regression is all you need". In this talk, we will introduce three ways to run regression models faster by using smarter algorithms, implemented in the scikit-learn & fastreg (sparse solvers), pyfixest (Frisch-Waugh-Lovell), and duckreg (regression compression via duckdb) libraries. https://www.youtube.com/watch?v=T4GTPqy0vjo https://www.youtube-nocookie.com/embed/T4GTPqy0vjo yes https://pycon.de/archive/2025/talks/a11y-need-is-love-but-accessible-docs-help-too/ 2024-12-18 https://pycon.de/static/media/social/talks/WLZSEZ.png A11y Need Is Love (But Accessible Docs Help Too) https://i.ytimg.com/vi/n3IeBB7Q9bw/maxresdefault.jpg A11y Need Is Love (But Accessible Docs Help Too) Accessible documentation benefits everyone, from developers to end users. Using the [PyData Sphinx Theme](https://pydata-sphinx-theme.readthedocs.io/en/stable/) as a case study, this talk dives into common accessibility barriers in documentation websites like low contrast colors, missing focus states, etc. and practical ways to address them. Learn about accessibility improvements and take part in a live accessibility audit to see how small changes can make a big difference. https://www.youtube.com/watch?v=n3IeBB7Q9bw https://www.youtube-nocookie.com/embed/n3IeBB7Q9bw yes https://pycon.de/archive/2025/talks/accuracy-is-not-enough-building-trustworthy-ai-with-conformal-prediction/ 2025-01-04 https://pycon.de/static/media/social/talks/UDDTBS.png Accuracy Is Not Enough: Building Trustworthy AI with Conformal Prediction https://i.ytimg.com/vi/n50E6k1h0HE/maxresdefault.jpg Accuracy Is Not Enough: Building Trustworthy AI with Conformal Prediction Building a good scoring model is just the beginning. In the age of critical AI applications, understanding and quantifying uncertainty is as crucial as achieving high accuracy. This talk highlights conformal prediction as the definitive approach to both uncertainty quantification and probability calibration, two extremely important topics in Deep Learning and Machine Learning. We’ll explore its theoretical underpinnings, practical implementations using TorchCP, and transformative impact on safety-critical fields like healthcare, robotics, and NLP. Whether you're building predictive systems or deploying AI in high-stakes environments, this session will provide actionable insights to level up your modelling skills for robust decision-making. https://www.youtube.com/watch?v=n50E6k1h0HE https://www.youtube-nocookie.com/embed/n50E6k1h0HE yes https://pycon.de/archive/2025/talks/agentic-ai-build-a-multi-agent-application-with-crewai/ 2024-12-23 https://pycon.de/static/media/social/talks/SVLRGG.png Agentic AI: Build a Multi-Agent Application with CrewAI https://i.ytimg.com/vi/Z4p-D6Qrp3g/maxresdefault.jpg Agentic AI: Build a Multi-Agent Application with CrewAI This hands-on tutorial will dive into the fundamentals of building multi-agent systems using the CrewAI Python library. Starting from the basics, we’ll cover key concepts, explore advanced features, and guide you step-by-step through building a complete application from scratch. We’ll discuss implementing guardrails, securing interactions, and preventing query injection vulnerabilities along the way. https://www.youtube.com/watch?v=Z4p-D6Qrp3g https://www.youtube-nocookie.com/embed/Z4p-D6Qrp3g yes https://pycon.de/archive/2025/talks/ai-agents-of-change-creating-reflecting-and-monetizing/ 2025-01-05 https://pycon.de/static/media/social/talks/PKZD8L.png AI Agents of Change: Creating, Reflecting, and Monetizing https://i.ytimg.com/vi/MNjCQSx0vfk/maxresdefault.jpg AI Agents of Change: Creating, Reflecting, and Monetizing Create, reflect, and earn—with purpose. In this workshop, you’ll not only build your own AI agent but also confront the ethical questions it raises, from its impact on jobs to its potential for social good. Together, we’ll explore how to harness AI for empowerment while uncovering pathways to turn your skills into meaningful value. This workshop is designed to equip Python enthusiasts with the tools to create their own AI agent while fostering a deeper understanding of the societal implications of this technology. Through hands-on learning, collaborative discussions, and practical monetization strategies, you’ll leave with more than just code—you’ll gain a vision of how AI can be wielded responsibly and profitably. https://www.youtube.com/watch?v=MNjCQSx0vfk https://www.youtube-nocookie.com/embed/MNjCQSx0vfk yes https://pycon.de/archive/2025/talks/ai-coding-agent-what-it-is-how-it-works-and-is-it-good-for-developers/ 2025-02-26 https://pycon.de/static/media/social/talks/UWTH7C.png AI coding agent - what it is, how it works and is it good for developers https://pycon.de/archive/2025/talks/ai-in-reality-fireside-chat-enterprise-ai-opensource-innovation/ 2025-04-17 https://pycon.de/static/media/social/talks/TAXVSC.png AI in Reality Fireside Chat: Enterprise AI & Open‑Source Innovation https://i.ytimg.com/vi/sAmh5S0MGhs/maxresdefault.jpg AI in Reality Fireside Chat: Enterprise AI & Open‑Source Innovation This fireside chat brings together leading voices from industry and open-source to explore how artificial intelligence is being meaningfully integrated into enterprise environments—beyond the buzzwords. Moderated by Alexander CS Hendorf, the conversation features Walid Mehanna (Chief Data Officer, Merck), Dr. Alexander Beck (CTO, Quoniam), and Ines Montani (co-founder explosion.ai, spaCy), who share their diverse perspectives from pharmaceuticals, finance, and AI tooling. Together, they’ll explore the cultural, technical, and ethical dimensions of AI adoption in large organizations, the growing influence of open-source ecosystems, and the long-term vision required to build sustainable, human-centered AI systems. This session is designed for those who want to move past the hype and better understand what real-world innovation at scale looks like—and what it demands from leadership, infrastructure, and community. https://www.youtube.com/watch?v=sAmh5S0MGhs https://www.youtube-nocookie.com/embed/sAmh5S0MGhs yes https://pycon.de/archive/2025/talks/algorithmic-music-composition-with-python/ 2024-12-20 https://pycon.de/static/media/social/talks/TQN98D.png Algorithmic Music Composition With Python https://i.ytimg.com/vi/37S0ZDpJpc4/maxresdefault.jpg Algorithmic Music Composition With Python Computers have long been an integral part of creating music. Virtual instruments and digital audio workstations make creating music easy and accessible. But how do programming languages and especially Python fit into this? Python can serve as a tool for creating musical notation and MIDI files. Throughout the session, you’ll learn how to: - Use Python to create melodies, harmonies, and rhythms. - Generate music based on rules, randomness, and mathematical principles. - Visualize and export your compositions as MIDI and sheet music. By the end of the talk, you’ll have a clear understanding of how to turn simple algorithms into expressive musical works. https://www.youtube.com/watch?v=37S0ZDpJpc4 https://www.youtube-nocookie.com/embed/37S0ZDpJpc4 yes https://pycon.de/archive/2025/talks/analyze-data-easily-with-duckdb-and-the-implications-on-data-architectures/ 2024-12-20 https://pycon.de/static/media/social/talks/TXKLWR.png Analyze data easily with duckdb - and the implications on data architectures https://i.ytimg.com/vi/z6-ati26i4Q/maxresdefault.jpg Analyze data easily with duckdb - and the implications on data architectures duckdb is increasingly becoming a universal tool for accessing and analyzing data. In this talk I will show with slides and live demo what duckdb is capable of and will dive deeper in how it will influence modern data architectures. https://www.youtube.com/watch?v=z6-ati26i4Q https://www.youtube-nocookie.com/embed/z6-ati26i4Q yes https://pycon.de/archive/2025/talks/are-llms-the-answer-to-all-our-problems/ 2024-12-17 https://pycon.de/static/media/social/talks/EN3QPQ.png Are LLMs the answer to all our problems? https://i.ytimg.com/vi/PIGd2lYX3N0/maxresdefault.jpg Are LLMs the answer to all our problems? Generative AI models have shaken up the German market. Since the release of ChatGPT, AI is available and usable for everyone. The number of ChatGPT-based agents is growing rapidly, but concerns about privacy, copyright and ethics remain. Regulation and ethical AI go hand in hand, but are often seen as barriers. The presentation will cover the different aspects of ethics and how they are addressed by regulation. It will give an overview of how to use large language models in a safe and practical way. This won't only address the various ethical issues, but also convince your next customer to invest in your AI-based product. https://www.youtube.com/watch?v=PIGd2lYX3N0 https://www.youtube-nocookie.com/embed/PIGd2lYX3N0 yes https://pycon.de/archive/2025/talks/autonomous-browsing-using-large-action-models/ 2024-12-20 https://pycon.de/static/media/social/talks/HYE8EX.png Autonomous Browsing using Large Action Models https://i.ytimg.com/vi/pjCG-LmKvKM/maxresdefault.jpg Autonomous Browsing using Large Action Models The browser serves as our gateway to the internet—the largest repository of knowledge in human history. Proficiency in its use is a core skill across nearly all professions and is becoming increasingly important for Artificial Intelligence. But can Large Action Models (LAMs) autonomously operate a browser? What exactly are LAMs that promise to translate human intentions into actions? We report on a project that fully automates the job application process using AI: from navigating unfamiliar website structures and filling out forms to handling document uploads and cookie banners. https://www.youtube.com/watch?v=pjCG-LmKvKM https://www.youtube-nocookie.com/embed/pjCG-LmKvKM yes https://pycon.de/archive/2025/talks/baybe-a-bayesian-back-end-for-experimental-planning-in-the-low-to-no-data-regime/ 2025-02-27 https://pycon.de/static/media/social/talks/TMBTYH.png BayBE: A Bayesian Back End for Experimental Planning in the Low-To-No-Data Regime https://i.ytimg.com/vi/EcVvYhh4wI0/maxresdefault.jpg BayBE: A Bayesian Back End for Experimental Planning in the Low-To-No-Data Regime From coffee machine settings to chemical reactions to website AB testing - iterative make-test-learn cycles are ubiquitous. The [Bayesian Back End](https://emdgroup.github.io/baybe/stable/) (BayBE) is an open-source experimental planner enabling users to smartly navigate such black-box optimization problems in iterative settings. This tutorial will i) introduce the core concepts enabled by combining Bayesian optimization and machine learning; ii) explain our software design choices, robust tests and open-source libraries this is built on; and iii) provide a short practical hands-on session. https://www.youtube.com/watch?v=EcVvYhh4wI0 https://www.youtube-nocookie.com/embed/EcVvYhh4wI0 yes https://pycon.de/archive/2025/talks/benchmarking-time-series-foundation-models-with-sktime/ 2024-12-22 https://pycon.de/static/media/social/talks/GUKTNX.png Benchmarking Time Series Foundation Models with sktime https://i.ytimg.com/vi/tbDgPppdvCU/maxresdefault.jpg Benchmarking Time Series Foundation Models with sktime Recent time series foundation models such as LagLlama, Chronos, Moirai, and TinyTimesMixer promise zero-shot forecasting for arbitrary time series. One central claim of foundation models is their ability to perform zero-shot forecasting, that is, to perform well with no training data. However, performance claims of foundation models are difficult to verify, as public benchmark datasets may have been a part of the training data, and only the already trained weights are available to the user. Therefore, performance in specific use cases must be verified based on the use case data itself to ensure a reliable assessment of forecasting performance. sktime allows users to easily produce a performance benchmark of any collection of forecasting models, foundation models, simple baselines, or custom methods on their internal use case data. https://www.youtube.com/watch?v=tbDgPppdvCU https://www.youtube-nocookie.com/embed/tbDgPppdvCU yes https://pycon.de/archive/2025/talks/beyond-agents-what-ai-strategy-really-needs-in-2025/ 2024-12-17 https://pycon.de/static/media/social/talks/JA9NFW.png Beyond Agents: What AI Strategy Really Needs in 2025 https://i.ytimg.com/vi/RViiO2o6RgA/maxresdefault.jpg Beyond Agents: What AI Strategy Really Needs in 2025 Artificial intelligence is no longer confined to models and APIs—it now shapes systems, hardware, and real-world agents. In this talk, I reflect on strategic insights gained at NVIDIA’s GTC 2025, where AI’s convergence with simulation, synthetic data, and robotics signals a fundamental shift. Drawing from over 1,100 sessions and personal experiences at the heart of Silicon Valley, I explore emerging patterns that redefine what it means to build and deploy AI at scale. We’ll look beyond the hype of large language models to examine autonomous systems, interdisciplinary development, and the infrastructure shifts enabling AI everywhere—from cloud to desktop. This session is a call to technical leaders and practitioners to broaden their perspective, think beyond tools, and engage strategically. Whether you’re developing agents, managing data pipelines, or scaling AI across teams, this talk will challenge assumptions and highlight what truly matters in 2025 and beyond. https://www.youtube.com/watch?v=RViiO2o6RgA https://www.youtube-nocookie.com/embed/RViiO2o6RgA yes https://pycon.de/archive/2025/talks/beyond-alembic-and-django-migrations/ 2024-12-26 https://pycon.de/static/media/social/talks/KCV9RS.png Beyond Alembic and Django Migrations https://i.ytimg.com/vi/gbiIkrHCZ_k/maxresdefault.jpg Beyond Alembic and Django Migrations ORMs like Django and SQLAlchemy have become indispensable in Python development, simplifying the interaction between applications and databases. Yet, their built-in schema migration tools often fall short in projects that require advanced database features or robust CI/CD integration. In this talk, we’ll explore how you can go beyond the limitations of your ORM’s migration tool. Using Atlas—a language-agnostic schema management tool—as a case study, we’ll demonstrate how Python developers can automate migration planning, leverage advanced database features, and seamlessly integrate database changes into modern CI/CD pipelines. https://www.youtube.com/watch?v=gbiIkrHCZ_k https://www.youtube-nocookie.com/embed/gbiIkrHCZ_k yes https://pycon.de/archive/2025/talks/beyond-basic-prompting-supercharging-open-source-llms-with-lmql-s-structured-generation/ 2024-12-22 https://pycon.de/static/media/social/talks/DQTMJB.png Beyond Basic Prompting: Supercharging Open Source LLMs with LMQL's Structured Generation https://i.ytimg.com/vi/Xd5nGnJv6KY/maxresdefault.jpg Beyond Basic Prompting: Supercharging Open Source LLMs with LMQL's Structured Generation This intermediate-level talk demonstrates how to leverage Language Model Query Language (LMQL) for structured generation and tool usage with open-source models like Llama. You will learn how to build a RAG system that enforces output constraints, handles tool calls, and maintains response structure - all while using open-source components. The presentation includes hands-on examples where audience members can experiment with LMQL prompts, showcasing real-world applications of constrained generation in production environments. https://www.youtube.com/watch?v=Xd5nGnJv6KY https://www.youtube-nocookie.com/embed/Xd5nGnJv6KY yes https://pycon.de/archive/2025/talks/beyond-dall-e-advanced-image-generation-workflows-with-comfyui/ 2024-12-22 https://pycon.de/static/media/social/talks/LRUKZQ.png Beyond DALL-E: Advanced Image Generation Workflows with ComfyUI https://i.ytimg.com/vi/EwuqWBIKhqs/maxresdefault.jpg Beyond DALL-E: Advanced Image Generation Workflows with ComfyUI Image generation using AI has made huge progress over the last years, and many people still think that DALL-E with a text prompt is the best way to generate images. There are well-known models like Stable Diffusion and Flux, which can be used with easy-to-use frontends like A1111 or Invoke AI, but if you want to do more complex or bleeding-edge workflows, you need something else. In this talk, I want to show you ComfyUI, an open-source node-based GUI written in Python where you can build complex pipelines that are otherwise only possible using plain code. https://www.youtube.com/watch?v=EwuqWBIKhqs https://www.youtube-nocookie.com/embed/EwuqWBIKhqs yes https://pycon.de/archive/2025/talks/beyond-fomo-keeping-up-to-date-in-ai/ 2024-12-22 https://pycon.de/static/media/social/talks/MSUCAS.png Beyond FOMO — Keeping Up-to-Date in AI https://i.ytimg.com/vi/6St1jRCUZT8/maxresdefault.jpg Beyond FOMO — Keeping Up-to-Date in AI The rapid evolution of AI technologies, particularly since the emergence of Large Language Models, has transformed the data science landscape from a field of steady progress to one of constant breakthroughs. This acceleration creates unique challenges for practitioners, from managing FOMO to battling imposter syndrome. Drawing from personal experience transitioning from mathematical modeling to modern AI development, this talk explores practical strategies for staying current while maintaining sanity. We'll discuss building effective learning structures, creating collaborative knowledge-sharing environments, and finding the right balance between innovation and implementation. Attendees will leave with actionable insights on navigating technological change while fostering sustainable growth in their teams and careers. https://www.youtube.com/watch?v=6St1jRCUZT8 https://www.youtube-nocookie.com/embed/6St1jRCUZT8 yes https://pycon.de/archive/2025/talks/bias-meets-bayes-a-bayesian-perspective-on-improving-model-fairness/ 2024-12-20 https://pycon.de/static/media/social/talks/ER3V7W.png Bias Meets Bayes: A Bayesian Perspective on Improving Model Fairness https://i.ytimg.com/vi/tH2iE7DWeCc/maxresdefault.jpg Bias Meets Bayes: A Bayesian Perspective on Improving Model Fairness Bias in machine learning models remains a pressing issue, often disproportionately affecting the most vulnerable groups in society. This talk introduces a Bayesian perspective to effectively tackle these challenges, focusing on improving fairness by modeling and addressing bias directly. You will learn about the interplay between uncertainty, equity, and predictive accuracy, while gaining actionable insights to improve fairness in diverse applications. Using a practical example of a risk-scoring model trained on data with underrepresented minority groups, I will showcase how Bayesian methods compare to traditional techniques, demonstrating their unique potential to mitigate bias while maintaining performance. https://www.youtube.com/watch?v=tH2iE7DWeCc https://www.youtube-nocookie.com/embed/tH2iE7DWeCc yes https://pycon.de/archive/2025/talks/blazing-fast-python-in-your-database-unlocking-data-science-at-scale-with-exasol/ 2025-04-09 https://pycon.de/static/media/social/talks/NQ3RHQ.png Blazing-Fast Python in Your Database: Unlocking Data Science at Scale with Exasol https://i.ytimg.com/vi/bNqg-JPzD70/maxresdefault.jpg Blazing-Fast Python in Your Database: Unlocking Data Science at Scale with Exasol What if your Python models could run inside your database—at scale, with parallel execution, and zero data movement? Meet Exasol: a high-performance Analytics Engine with native Python support and a massively parallel processing (MPP) engine. In this session, you’ll learn how to run Python directly where your data lives using user-defined functions (UDFs) and customizable script language containers. Whether you're doing forecasting, categorization, or calling APIs in real time, Exasol enables fast, scalable Python execution—perfect for demanding data science workflows. We’ll share real-world use cases, including large-scale model inference across thousands of sensors. If you're tired of bottlenecks and batch jobs, this is your shortcut to blazing-fast, in-database Python. https://www.youtube.com/watch?v=bNqg-JPzD70 https://www.youtube-nocookie.com/embed/bNqg-JPzD70 yes https://pycon.de/archive/2025/talks/bridging-the-gap-unlocking-sap-data-for-data-lakes-with-python-and-pyspark-via-sap-datasphere/ 2024-12-22 https://pycon.de/static/media/social/talks/7CL3KS.png Bridging the gap: unlocking SAP data for data lakes with Python and PySpark via SAP Datasphere https://pycon.de/archive/2025/talks/build-a-personalized-commute-agent-in-python-with-hopsworks-langgraph-and-llm-function-calling/ 2025-01-05 https://pycon.de/static/media/social/talks/8S3RC3.png Build a personalized Commute agent in Python with Hopsworks, LangGraph and LLM Function Calling https://i.ytimg.com/vi/terv7KGaipM/maxresdefault.jpg Build a personalized Commute agent in Python with Hopsworks, LangGraph and LLM Function Calling The invention of the clock and the organization of time in zones have helped synchronize human activities across the globe. While timekeepers are better at planning and sticking to the plan, time optimists somehow believe that time is malleable and extends the closer the deadline. Nevertheless, whether you are an organized timekeeper or a creative timebender, external factors can affect your commute. In this talk, we will define the different components necessary to build a personalized commute virtual agent in Python. The agent will help you analyze your historical lateness records, estimate future delays, and suggest the best time to leave home based on these predictions. It will be powered by a LLM and will use a technique called Function Calling to recognize the user intent from the conversation history and provide informed answers. https://www.youtube.com/watch?v=terv7KGaipM https://www.youtube-nocookie.com/embed/terv7KGaipM yes https://pycon.de/archive/2025/talks/building-a-hybridrag-document-question-answering-system/ 2025-01-03 https://pycon.de/static/media/social/talks/9CRNU3.png Building a HybridRAG Document Question-Answering System https://i.ytimg.com/vi/KTpG-T3DK6k/maxresdefault.jpg Building a HybridRAG Document Question-Answering System Retrieval Augmented Generation (RAG) is a powerful technique for searching across unstructured documents, but it often falls short when the task demands an understanding of intricate relationships between entities. GraphRAG addresses this by leveraging knowledge graphs to capture these relationships, but it struggles with scalability and handling diverse unstructured formats. In this talk, we’ll explore how HybridRAG combines the strengths of both approaches - RAG for scalable unstructured data retrieval and GraphRAG for semantic richness- to deliver accurate and contextually relevant answers. We’ll dive into its application, challenges, and the significant improvements it offers for question-answering systems across various domains. https://www.youtube.com/watch?v=KTpG-T3DK6k https://www.youtube-nocookie.com/embed/KTpG-T3DK6k yes https://pycon.de/archive/2025/talks/building-a-self-hosted-mlops-platform-with-kubernetes/ 2024-12-22 https://pycon.de/static/media/social/talks/3CYZUH.png Building a Self-Hosted MLOps Platform with Kubernetes https://i.ytimg.com/vi/ZAW99CCc6Ok/maxresdefault.jpg Building a Self-Hosted MLOps Platform with Kubernetes Many managed MLOps platforms, while convenient, often fall short in providing flexibility, requiring complex integrations, and causing vendor lock-in. In this talk, we’ll share our experience transitioning from managed MLOps tools to a self-hosted solution built on Kubernetes. We’ll focus on how we leveraged open-source tools like Feast, MLflow, and Ray to build a more flexible, scalable, and customizable platform that is now in use at Rewe Digital. By migrating to this self-hosted architecture, we gained greater control over our ML pipelines, reduced our dependency on third-party services, and created a more adaptable infrastructure for our ML workloads. https://www.youtube.com/watch?v=ZAW99CCc6Ok https://www.youtube-nocookie.com/embed/ZAW99CCc6Ok yes https://pycon.de/archive/2025/talks/building-an-open-source-rag-system-for-the-united-nations-negotiations-on-global-plastic-pollution/ 2024-12-20 https://pycon.de/static/media/social/talks/NF8UPF.png Building an Open Source RAG System for the United Nations Negotiations on Global Plastic Pollution https://i.ytimg.com/vi/yXpHtgXsMz0/maxresdefault.jpg Building an Open Source RAG System for the United Nations Negotiations on Global Plastic Pollution Plastic pollution is a significant global challenge. Every year, millions of tons of plastic enter the oceans, impacting marine ecosystems and human health. To address this issue, the United Nations is negotiating a legally binding treaty with representatives from 180 countries, aiming to reduce plastic pollution and promote sustainable practices. We have developed NegotiateAI, an open-source chat application that supports delegations during the UN negotiations on a legally binding agreement to combat plastic pollution. The tool demonstrates how generative AI and Retrieval Augmented Systems (RAG) can address complex global challenges. Built with Haystack 2.0, Qdrant, HuggingFace Spaces, and Streamlit, it showcases the potential of open-source technologies in tackling issues of global relevance. As a beginner or advanced developer, this talk will give you valuable insights into developing impactful AI applications with open source tools in the public sector. https://www.youtube.com/watch?v=yXpHtgXsMz0 https://www.youtube-nocookie.com/embed/yXpHtgXsMz0 yes https://pycon.de/archive/2025/talks/building-bare-bones-game-physics-in-rust-with-python-integration/ 2024-11-28 https://pycon.de/static/media/social/talks/VKYDBD.png Building Bare-Bones Game Physics in Rust with Python Integration https://i.ytimg.com/vi/GA3NU7Tmb2U/maxresdefault.jpg Building Bare-Bones Game Physics in Rust with Python Integration Learn how to build a minimalist game physics engine in Rust and make it accessible to Python developers using PyO3. This talk explores fundamental concepts like collision detection and motion dynamics while focusing on Python integration for scripting and testing. Ideal for developers interested in combining Rust’s performance with Python’s ease of use to create lightweight and efficient tools for games or simulations. https://www.youtube.com/watch?v=GA3NU7Tmb2U https://www.youtube-nocookie.com/embed/GA3NU7Tmb2U yes https://pycon.de/archive/2025/talks/building-reliable-ai-agents-for-publishing-a-dspy-based-quality-assurance-framework/ 2025-01-04 https://pycon.de/static/media/social/talks/F7RDPT.png Building Reliable AI Agents for Publishing: A DSPy-Based Quality Assurance Framework https://i.ytimg.com/vi/J2lUeEM9Bwg/maxresdefault.jpg Building Reliable AI Agents for Publishing: A DSPy-Based Quality Assurance Framework As publishers increasingly adopt AI agents for content generation and analysis, ensuring output quality and reliability becomes critical. This talk introduces a novel quality assurance framework built with DSPy that addresses the unique challenges of evaluating AI agents in publishing workflows. Using real-world examples from newsroom implementations, I will demonstrate how to design and implement systematic testing pipelines that verify factual accuracy, content consistency, and compliance with editorial standards. Attendees will learn practical techniques for building reliable agent evaluation systems that go beyond simple metrics to ensure AI-generated content meets professional publishing standards. https://www.youtube.com/watch?v=J2lUeEM9Bwg https://www.youtube-nocookie.com/embed/J2lUeEM9Bwg yes https://pycon.de/archive/2025/talks/building-serverless-python-ai-skills-as-wasm-components/ 2025-04-03 https://pycon.de/static/media/social/talks/LYDDDC.png Building Serverless Python AI skills as WASM components https://i.ytimg.com/vi/_MuCOm9Ebk8/maxresdefault.jpg Building Serverless Python AI skills as WASM components Frameworks like llama-stack and langchain allow for quick prototyping of generative AI applications. However, companies often struggle to deploy these applications into production quickly. This talk explores the design of a Python SDK that enables the development of AI skills in Python and their compilation into WebAssembly (WASM) components, targeting a specific host runtime that offers interfaces for interacting with LLMs and associated tooling. https://www.youtube.com/watch?v=_MuCOm9Ebk8 https://www.youtube-nocookie.com/embed/_MuCOm9Ebk8 yes https://pycon.de/archive/2025/talks/building-versatile-operating-setups-for-real-world-use-and-testing-with-python-and-the-raspberry-pi/ 2025-03-18 https://pycon.de/static/media/social/talks/PW3VKG.png Building versatile operating setups for real world use and testing with Python and the Raspberry Pi https://i.ytimg.com/vi/C-Jle-23rQ0/maxresdefault.jpg Building versatile operating setups for real world use and testing with Python and the Raspberry Pi **Rosenxt** is the host of a number of ventures aiming to provide next level solutions for demanding problems in a variety of industries based on decades of engineering excellence. Some of them address challenges in water environments ranging from water pipelines to offshore applications. As differing as these areas may seem, regarding the solutions we build for them they have a lot in common. Whether its the necessary power supply, movement and steering concepts or sensing approaches. All of them benefit from generalized, smart solutions that we design as components that can later be orchestrated and configured in various setups to fulfill quite different purposes. This presentation explores the versatility of leveraging a Raspberry Pi based hardware platform combined with a Python based application stack to bridge development and deployment of various basic components, such as motors and motor controllers, lift foils, steering units and controls. By utilizing a unified platform, we demonstrate how the same system can seamlessly transition from test bench measurements during hardware component development to real-world applications for various industries. The talk highlights how this approach can create a robust framework to help streamlining workflows, enhance scalability and reduce costs. https://www.youtube.com/watch?v=C-Jle-23rQ0 https://www.youtube-nocookie.com/embed/C-Jle-23rQ0 yes https://pycon.de/archive/2025/talks/cache-me-if-you-can-boosted-application-performance-with-redis-and-client-side-caching/ 2025-04-01 https://pycon.de/static/media/social/talks/3FSWJU.png Cache me if you can: Boosted application performance with Redis and client-side caching https://i.ytimg.com/vi/lzxh1ZKVFqU/maxresdefault.jpg Cache me if you can: Boosted application performance with Redis and client-side caching Did you know Redis can notify your app about server-side data changes? This feature enables client-side tracking and caching in redis-py, helping to reduce network round-trips and optimize performance. In this talk, we explore how client-side caching works in redis-py and how you can use it to make your applications even faster. https://www.youtube.com/watch?v=lzxh1ZKVFqU https://www.youtube-nocookie.com/embed/lzxh1ZKVFqU yes https://pycon.de/archive/2025/talks/career-path-experience-stories/ 2025-04-07 https://pycon.de/static/media/social/talks/XB8VG7.png Career Path Experience Stories https://i.ytimg.com/vi/EFMysngXGRc/maxresdefault.jpg Career Path Experience Stories As part of the PyConDE & PyData 2025 Conference, we would like to present an initiative aimed primarily at students and those just starting their careers in computer science. Our goal is to showcase the diverse career paths possible and break some myths about typical job skills and responsibilities relevant, so as to inspire and encourage their journey. https://www.youtube.com/watch?v=EFMysngXGRc https://www.youtube-nocookie.com/embed/EFMysngXGRc yes https://pycon.de/archive/2025/talks/challenges-and-lessons-learned-while-building-a-real-time-lakehouse-using-apache-iceberg-and-kafka/ 2024-12-19 https://pycon.de/static/media/social/talks/JUAF3S.png Challenges and Lessons Learned While Building a Real-Time Lakehouse using Apache Iceberg and Kafka https://pycon.de/archive/2025/talks/chasing-the-dark-universe-with-euclid-and-python-unveiling-the-secrets-of-the-cosmos/ 2025-02-19 https://pycon.de/static/media/social/talks/EGNBHD.png Chasing the Dark Universe with Euclid and Python: Unveiling the Secrets of the Cosmos https://i.ytimg.com/vi/iHt3-HeLcLc/maxresdefault.jpg Chasing the Dark Universe with Euclid and Python: Unveiling the Secrets of the Cosmos The ESA Euclid mission, launched in July 2023, is on a quest to unravel the mysteries of dark energy and dark matter: the enigmatic components that make up 95% of the Universe. By mapping one-third of the sky with unprecedented precision, Euclid is building the largest 3D map of the cosmos. This talk explores how cosmologists bridge theory and and Euclid observation to reveal the hidden nature of dark energy and the dark matter. We will delve into the challenges of cosmological inference, where advanced statistical methods and Python-based pipelines compare theoretical models against Euclid's vast datasets, and we will explain how Bayesian inference, machine learning, and state-of-the-art simulations are revolutionizing our understanding of the cosmos. https://www.youtube.com/watch?v=iHt3-HeLcLc https://www.youtube-nocookie.com/embed/iHt3-HeLcLc yes https://pycon.de/archive/2025/talks/citation-is-collaboration-software-recognition-in-research-and-industry/ 2024-12-22 https://pycon.de/static/media/social/talks/VJR39N.png Citation is Collaboration: Software Recognition in Research and Industry https://i.ytimg.com/vi/E7p6Ata8SfY/maxresdefault.jpg Citation is Collaboration: Software Recognition in Research and Industry The development of open source software is increasingly recognized as a critical contribution across many disciplines, yet the mechanisms for credit and citation vary significantly. This talk uses astronomy as a case study to explore shared challenges in attributing software contributions across research and industry. It will review the evolution of journal recommendations and policies over the past decade, alongside emerging publishing practices offering insights into their impact on the recognition of software contributions. An analysis of citation patterns for widely used libraries (numpy, scipy, astropy) highlights trends over time and their dependence on publication venues and policies. The talk will conclude with strategies for both developers and users for improving the recognition of software, fostering collaboration and sustainability in software ecosystems. All data and analysis code will be made available in a public repository, supporting transparency and further study. https://www.youtube.com/watch?v=E7p6Ata8SfY https://www.youtube-nocookie.com/embed/E7p6Ata8SfY yes https://pycon.de/archive/2025/talks/code-community-the-synergy-of-community-building-and-task-automation/ 2024-12-17 https://pycon.de/static/media/social/talks/PLMJZ8.png Code & Community: The Synergy of Community Building and Task Automation https://i.ytimg.com/vi/N2Wx1XZ__M0/maxresdefault.jpg Code & Community: The Synergy of Community Building and Task Automation The Python community is built on a culture of support, inclusion, and collaboration. Sustaining this welcoming environment requires intentional community-building efforts, which often involve repetitive or time-consuming tasks. These tasks, however, can be automated without compromising their value—freeing up time for meaningful human engagement. This talk showcases my project aimed at supporting underrepresented groups in tech, specifically through building Python communities on Mastodon and Bluesky. A key part of this initiative is the "Awesome PyLadies" repository, a curated collection of PyLadies blogs and YouTube channels that celebrates their work. To enhance visibility, I created a PyLadies bot for social media. This bot automates regular posts and reposts tagged content, significantly extending their reach and fostering an engaged community. In this session, I’ll cover: - The role of automation in community building - The technical architecture behind the bot - A hands-on demo on integrating Google’s Gemini into community tools - Upcoming features and opportunities for collaboration By combining Python, automation, and modern AI capabilities, we can create thriving, inclusive communities that scale impact while staying true to the human-centered ethos of open source. https://www.youtube.com/watch?v=N2Wx1XZ__M0 https://www.youtube-nocookie.com/embed/N2Wx1XZ__M0 yes https://pycon.de/archive/2025/talks/composable-ai-building-next-gen-ai-agents-with-mcp/ 2025-03-27 https://pycon.de/static/media/social/talks/7FLW7F.png Composable AI: Building Next-Gen AI Agents with MCP https://i.ytimg.com/vi/88IvVleJXmo/maxresdefault.jpg Composable AI: Building Next-Gen AI Agents with MCP At Blue Yonder, we're embarking on a journey toward building composable AI agents using Model Context Protocol (MCP). We're discovering firsthand the challenges of integrating diverse products and APIs into useful, context-aware agents. In this talk, I'll discuss our early experiences, the challenges we've faced, and why MCP is emerging as a potential game changer for developing scalable, flexible AI solutions. https://www.youtube.com/watch?v=88IvVleJXmo https://www.youtube-nocookie.com/embed/88IvVleJXmo yes https://pycon.de/archive/2025/talks/conformal-prediction-uncertainty-quantification-to-humanise-models/ 2024-12-13 https://pycon.de/static/media/social/talks/FGEUJJ.png Conformal Prediction: uncertainty quantification to humanise models https://i.ytimg.com/vi/EJVD-jU1A44/maxresdefault.jpg Conformal Prediction: uncertainty quantification to humanise models Quantifying model uncertainties is critical to improve model reliability and make sound decisions. Conformal Prediction is a framework for uncertainty quantification that provides mathematical guarantees of true outcome coverage, allowing more informed decisions to be made by stakeholders https://www.youtube.com/watch?v=EJVD-jU1A44 https://www.youtube-nocookie.com/embed/EJVD-jU1A44 yes https://pycon.de/archive/2025/talks/conquering-pdfs-document-understanding-beyond-plain-text/ 2024-11-28 https://pycon.de/static/media/social/talks/FUX3FR.png Conquering PDFs: document understanding beyond plain text https://i.ytimg.com/vi/lRyPdDqgqcw/maxresdefault.jpg Conquering PDFs: document understanding beyond plain text NLP and data science could be so easy if all of our data came as clean and plain text. But in practice, a lot of it is hidden away in PDFs, Word documents, scans and other formats that have been a nightmare to work with. In this talk, I'll present a new and modular approach for building robust document understanding systems, using state-of-the-art models and the awesome Python ecosystem. I'll show you how you can go from PDFs to structured data and even build fully custom information extraction pipelines for your specific use case. https://www.youtube.com/watch?v=lRyPdDqgqcw https://www.youtube-nocookie.com/embed/lRyPdDqgqcw yes https://pycon.de/archive/2025/talks/conquering-the-queue-lessons-from-processing-one-billion-celery-tasks/ 2024-12-22 https://pycon.de/static/media/social/talks/J8FLDN.png Conquering the Queue: Lessons from processing one billion Celery tasks https://i.ytimg.com/vi/mb7a_WErTck/maxresdefault.jpg Conquering the Queue: Lessons from processing one billion Celery tasks At Userlike, Celery is the backbone of our application, orchestrating over a 100 million tasks per month. In this talk, I’ll share real-world insights into scaling Celery, optimizing performance, avoiding common pitfalls, handling failures, and building a resilient architecture. https://www.youtube.com/watch?v=mb7a_WErTck https://www.youtube-nocookie.com/embed/mb7a_WErTck yes https://pycon.de/archive/2025/talks/data-as-python-code/ 2024-12-19 https://pycon.de/static/media/social/talks/SXRVNU.png Data as (Python) Code https://i.ytimg.com/vi/dnY_80XxpDE/maxresdefault.jpg Data as (Python) Code In contemporary data-driven environments, the seamless integration of data into automated workflows is paramount. The reliability of automation, however, is constantly threatened by breaking changes in the source data. The Data-as-Code (DaC) paradigm address this challenge by treating data as a first-class citizen within the software development lifecycle. https://www.youtube.com/watch?v=dnY_80XxpDE https://www.youtube-nocookie.com/embed/dnY_80XxpDE yes https://pycon.de/archive/2025/talks/dataframely-a-declarative-native-data-frame-validation-library/ 2025-03-14 https://pycon.de/static/media/social/talks/3DCS8K.png Dataframely — A declarative, 🐻‍❄️-native data frame validation library https://i.ytimg.com/vi/qKrt7U2tTmA/maxresdefault.jpg Dataframely — A declarative, 🐻‍❄️-native data frame validation library Understanding the structure and content of data frames is crucial when working with tabular data — a core requirement for the robust pipelines we build at QuantCo. Libraries such as `pandera` or `patito` already exist to ease the process of defining data frame schemas and validating that data frames comply with these schemas. However, when building production-ready data pipelines, we encountered limitations of these libraries. Specifically, we were missing support for strict static type checking, validation of interdependent data frames, and graceful validation including introspection of failures. To remedy the shortcomings of these libraries, we started building `dataframely` at the beginning of last year. Dataframely is a declarative data frame validation library with first-class support for polars data frames. Over the last year, we have gained experience in using `dataframely` both for analytical and production code across several projects. The result was a drastic improvement of the legibility of our pipeline code and our confidence in its correctness. To enable the wider data engineering community to benefit from similar effects, we have recently open-sourced `dataframely` and are keen on introducing it in this talk. https://www.youtube.com/watch?v=qKrt7U2tTmA https://www.youtube-nocookie.com/embed/qKrt7U2tTmA yes https://pycon.de/archive/2025/talks/death-by-a-thousand-api-versions/ 2024-12-21 https://pycon.de/static/media/social/talks/ZMKJAY.png Death by a Thousand API Versions https://i.ytimg.com/vi/kzpcqAiveyg/maxresdefault.jpg Death by a Thousand API Versions API versioning is tough, really tough. We tried multiple approaches to versioning in production and eventually ended up with a solution we love. During this talk you will look into the tradeoffs of the most popular ways to do API versioning, and I will recommend which ones are fit for which products and companies. I will also present my framework, Cadwyn, that allows you to support hundreds of API versions with ease -- based on FastAPI and inspired by Stripe's approach to API versioning. After this session, you will understand which approach to pick for your company to make your versioning cost effective and maintainable without investing too much into it. https://www.youtube.com/watch?v=kzpcqAiveyg https://www.youtube-nocookie.com/embed/kzpcqAiveyg yes https://pycon.de/archive/2025/talks/decoding-topics-a-comparative-analysis-of-pythons-leading-topic-modeling-libraries-using-climate-c/ 2024-11-28 https://pycon.de/static/media/social/talks/BJKSGK.png Decoding Topics: A Comparative Analysis of Python’s Leading Topic Modeling Libraries Using Climate C https://i.ytimg.com/vi/d2qpJPDsJEs/maxresdefault.jpg Decoding Topics: A Comparative Analysis of Python’s Leading Topic Modeling Libraries Using Climate C Topic modelling has come a long way, evolving from traditional statistical methods to leveraging advanced embeddings and neural networks. Python’s diverse library ecosystem includes tools like Latent Dirichlet Allocation (LDA) using gensim, Top2Vec, BERTopic, and Contextualized Topic Models (CTM). This talk evaluates these popular approaches using a dataset of UK climate change policies, considering use cases relevant to organisations like DEFRA (Department for Environment, Food & Rural Affairs). The analysis explores real-time integration, dynamic topic modelling over time, adding new documents, and retrieving similar ones. Attendees will learn the strengths, limitations, and practical applications of each library to make informed decisions for their projects. https://www.youtube.com/watch?v=d2qpJPDsJEs https://www.youtube-nocookie.com/embed/d2qpJPDsJEs yes https://pycon.de/archive/2025/talks/demystifying-design-patterns-a-practical-guide-for-developers/ 2024-12-22 https://pycon.de/static/media/social/talks/8PFFPS.png Demystifying Design Patterns: A Practical Guide for Developers https://i.ytimg.com/vi/4YmFBjnOeQM/maxresdefault.jpg Demystifying Design Patterns: A Practical Guide for Developers Do you ever worry about your code becoming spaghetti-like and difficult to maintain? Master the art of crafting clean, maintainable, and adaptable software by harnessing the power of design patterns. This presentation will empower you with a clear, structured understanding of these reusable solutions to address common programming challenges. We'll delve into design patterns’ key categories: Behavioral, Structural, and Creational, as well as explore their functionality and how they can be applied in your daily development workflow. For each category, we'll also explore a practical design pattern in detail and showcase real-world applications of these patterns, along with small-scale code examples that illustrate their practical implementation. You'll gain valuable insight into how these patterns can translate into real-world development scenarios, such as facilitating communication between objects (Behavioral), separating interfaces from implementation for flexibility (Structural), and enabling dynamic algorithm selection at runtime (Creational). https://www.youtube.com/watch?v=4YmFBjnOeQM https://www.youtube-nocookie.com/embed/4YmFBjnOeQM yes https://pycon.de/archive/2025/talks/deploying-synchronous-and-asynchronous-django-applications-for-hobby-projects/ 2024-11-30 https://pycon.de/static/media/social/talks/CMTKZS.png Deploying Synchronous and Asynchronous Django Applications for Hobby Projects https://i.ytimg.com/vi/9nTNFtmNqbQ/maxresdefault.jpg Deploying Synchronous and Asynchronous Django Applications for Hobby Projects Simplify deploying hybrid Django applications with synchronous views and asynchronous apps. This session covers ASGI support, Docker containerization, and Kamal for seamless, zero-downtime deployments on single-server setups, ideal for hobbyists and small-scale projects. https://www.youtube.com/watch?v=9nTNFtmNqbQ https://www.youtube-nocookie.com/embed/9nTNFtmNqbQ yes https://pycon.de/archive/2025/talks/design-generate-deploy-contract-first-with-fastapi/ 2024-12-19 https://pycon.de/static/media/social/talks/ZACM3E.png Design, Generate, Deploy: Contract-First with FastAPI https://i.ytimg.com/vi/7x1cGhyjv7c/maxresdefault.jpg Design, Generate, Deploy: Contract-First with FastAPI This talk explores a contract-first approach to API development using the OpenAPI generator, a powerful tool for automating API generation from a standardized specification. We will cover (1) what would you need to run to have a standard implementation of the FastAPI endpoints and data models; (2) how to customize the mustache templates that are used to generate the API stubs; (3) share some ideas how to customize the CLI and (4) how to maintain the contract and how to handle breaking changes to the contract. We will close the session with a discussion of the challenges of implementing the OpenAPI generator. https://www.youtube.com/watch?v=7x1cGhyjv7c https://www.youtube-nocookie.com/embed/7x1cGhyjv7c yes https://pycon.de/archive/2025/talks/distributed-file-systems-made-easy-with-python-s-fsspec/ 2024-12-17 https://pycon.de/static/media/social/talks/DEHZHK.png Distributed file-systems made easy with Python's fsspec https://i.ytimg.com/vi/0ya1Zeb5ONk/maxresdefault.jpg Distributed file-systems made easy with Python's fsspec The cloud native revolution has impacted all aspects of engineering, and data engineering is not exempt. One of the ongoing challenges in the data engineering world remains the local and distributed cloud native storage. In this talk we’ll explore working with distributed file systems in Python, through an intro to fsspec: a popular python library that is well-positioned to address the growing challenge of interacting with storage systems of different kinds in a consistent way. In this talk we’ll show hands-on examples of working with fsspec with some of the most popular data tools in the Python community: Pandas, Tensorflow and PyArrow. We’ll demonstrate a real world implementation of fsspec and how it provides easy extensibility through open source tooling. You’ll come away from this session with a better understanding for how to implement and extend fsspec to work with different cloud native storage systems. https://www.youtube.com/watch?v=0ya1Zeb5ONk https://www.youtube-nocookie.com/embed/0ya1Zeb5ONk yes https://pycon.de/archive/2025/talks/driving-trust-and-addressing-ethical-challenges-in-transportation-through-explainable-ai/ 2025-01-05 https://pycon.de/static/media/social/talks/NPMNCE.png Driving Trust and Addressing Ethical Challenges in Transportation through Explainable AI https://i.ytimg.com/vi/mn-Cnuz9Kno/maxresdefault.jpg Driving Trust and Addressing Ethical Challenges in Transportation through Explainable AI Machine Learning can transform transportation—improving safety, optimizing routes, and reducing delays—yet it also presents ethical concerns. In this talk,I will show how Explainable AI (XAI) can offer practical solutions these ethical dilemmas like lack of trust in AI solutions. Instead of focusing on the technical underpinnings, we will discuss how transparency can be enhanced in AI-supported transportation systems. Using a real-world example, I will demonstrate how XAI provides the groundwork for building ethical, trustworthy, and socially responsible AI solutions in public transportation systems. https://www.youtube.com/watch?v=mn-Cnuz9Kno https://www.youtube-nocookie.com/embed/mn-Cnuz9Kno yes https://pycon.de/archive/2025/talks/duplicate-code-dilemma-unlocking-automation-with-open-source/ 2024-12-16 https://pycon.de/static/media/social/talks/KZKT9W.png Duplicate Code Dilemma: Unlocking Automation with Open Source! https://i.ytimg.com/vi/x6UptDWh7_k/maxresdefault.jpg Duplicate Code Dilemma: Unlocking Automation with Open Source! "Don't Repeat Yourself" – a phrase that we have all heard many times. In this talk, we will have an overview how to deal with code duplication and how open-source template libraries such as Copier can assist us in managing similarly structured repositories. Furthermore, we will explore how code updates can be automated with the help of open-source libraries like Renovate Bot. By the end of this session, you will gain insights into these solutions while also questioning whether they truly eliminate repetition or merely contribute to another cycle of automation. https://www.youtube.com/watch?v=x6UptDWh7_k https://www.youtube-nocookie.com/embed/x6UptDWh7_k yes https://pycon.de/archive/2025/talks/electify-retrieval-augmented-generation-for-voter-information-in-the-2024-european-election/ 2025-01-03 https://pycon.de/static/media/social/talks/DNVCEY.png Electify - Retrieval-Augmented Generation for Voter Information in the 2024 European Election https://i.ytimg.com/vi/0w2q22jUqmk/maxresdefault.jpg Electify - Retrieval-Augmented Generation for Voter Information in the 2024 European Election In general elections, voters often face the challenge of navigating complex political landscapes and extensive party manifestos. To address this, we developed Electify, an interactive application that utilizes Retrieval-Augmented Generation (RAG) to provide concise summaries of political party positions based on individual user queries. During its first roll-out for the European Election 2024, Electify attracted more than 6,000 active users. This talk will explore its development and deployment. It will focus on its technical architecture, the integration of data from party manifestos and parliamentary speeches, and the challenges of ensuring political neutrality and providing accurate replies. Additionally, we will discuss user feedback and ethical considerations, focusing on how generative AI can enhance voter information systems. https://www.youtube.com/watch?v=0w2q22jUqmk https://www.youtube-nocookie.com/embed/0w2q22jUqmk yes https://pycon.de/archive/2025/talks/enhancing-rag-with-fast-graphrag-and-instructlab-a-scalable-interpretable-and-efficient-framework/ 2024-12-21 https://pycon.de/static/media/social/talks/JABVHK.png Enhancing RAG with Fast GraphRAG and InstructLab: A Scalable, Interpretable, and Efficient Framework https://i.ytimg.com/vi/OxlsHjGxMQ8/maxresdefault.jpg Enhancing RAG with Fast GraphRAG and InstructLab: A Scalable, Interpretable, and Efficient Framework Retrieval Augmented Generation (RAG) has become a cornerstone in enriching GenAI outputs with external data, yet traditional frameworks struggle with challenges like data noise, domain specialization, and scalability. In this talk, Tuhin will dive into open-source frameworks Fast GraphRAG and InstructLab, which addresses these limitations by combining knowledge graphs with the classical PageRank algorithm and Fine-tuning, delivering a precision-focused, scalable, and interpretable solution. By leveraging the structured context of knowledge graphs, Fast GraphRAG enhances data adaptability, handles dynamic datasets efficiently, and provides traceable, explainable outputs while InstructLab adds domain depth to the LLM through Fine-tuning. Designed for real-world applications, it bridges the gap between raw data and actionable insights, redefining intelligent retrieval for developers, researchers, and enterprises. This talk will showcase Fast GraphRAG’s transformative features coupled with domain specific Fine-tuning leveraging InstructLab and demonstrate its potential to elevate RAG’s capabilities in handling the evolving demands of large language models (LLMs) for developers, researchers, and businesses. https://www.youtube.com/watch?v=OxlsHjGxMQ8 https://www.youtube-nocookie.com/embed/OxlsHjGxMQ8 yes https://pycon.de/archive/2025/talks/enhancing-software-supply-chain-security-with-open-source-python-tools/ 2024-12-20 https://pycon.de/static/media/social/talks/M98YBR.png Enhancing Software Supply Chain Security with Open Source Python Tools https://i.ytimg.com/vi/NnxAHdLY058/maxresdefault.jpg Enhancing Software Supply Chain Security with Open Source Python Tools The Cyber Resilience Act (CRA) is focused on improving the security and resilience of digital products. But to comply with the CRA, businesses will need to start preparing the necessary evidence to ensure compliance if they want to continue to deliver digital products to the EU market once the CRA is in force. Key requirements within the CRA include implementing robust security measures throughout the product life-cycle, adopting secure development practices and implementing proactive vulnerability management processes. This session will show how a number of the requirements for the CRA can be achieved by use of a number of open source Python tools. https://www.youtube.com/watch?v=NnxAHdLY058 https://www.youtube-nocookie.com/embed/NnxAHdLY058 yes https://pycon.de/archive/2025/talks/expectation-a-modern-take-on-statistical-a-b-testing-with-e-values-and-martingales/ 2024-12-19 https://pycon.de/static/media/social/talks/ZKNTGN.png expectation: A modern take on statistical A/B testing with e-values and martingales https://pycon.de/archive/2025/talks/extending-python-with-rust-mojo-cuda-and-c-and-building-packages/ 2024-12-21 https://pycon.de/static/media/social/talks/P9VKRV.png Extending Python with Rust, Mojo, Cuda and C and building packages https://i.ytimg.com/vi/iiYKsL3YRaE/maxresdefault.jpg Extending Python with Rust, Mojo, Cuda and C and building packages We all love Python - but we especially love it for its unique ability as a glue language. In this talk we will show a number of ways of extending Python: using Rust, C and Cython, C++, CUDA and Mojo! We will use the pixi package manager and the open source conda-forge distribution to demonstrate how to easily build custom Python extensions with these languages. The main challenge with custom extensions is about distributing them. The new pixi build feature makes it easy to build a Python extension into a conda package as well as wheel file for PyPI. Pixi will manage not only Python, but also the compilers and other system-level dependencies. https://www.youtube.com/watch?v=iiYKsL3YRaE https://www.youtube-nocookie.com/embed/iiYKsL3YRaE yes https://pycon.de/archive/2025/talks/fasthtml-vs-streamlit-the-dashboarding-face-off/ 2024-12-04 https://pycon.de/static/media/social/talks/WCDPLP.png FastHTML vs. Streamlit - The Dashboarding Face Off https://i.ytimg.com/vi/H7FCDLqEpk8/maxresdefault.jpg FastHTML vs. Streamlit - The Dashboarding Face Off In the right corner, we have the go-to dashboarding solution for showcasing ML models or visualizing data, **STREAMLIT** (\*crowd cheers\*). Simple yet powerful, it defends the throne of Python dashboarding, but have you ever tried to create complex interactions with it? Things like drill-downs or logins, can make your control flow become messy really quick (\*crowd nods knowlingly\*). And in the left corner, the new contender in the arena of Python web frameworks which, according to its docs, "*excels at building dashboards*", **FastHTML** (\*crowd whoops\*). We will see if this is true, in the **ultimate dashboarding face off** (\*crowd gasps\*). By building the same dashboard, step by step, in both frameworks, investigate their strengths and weaknesses, we will see which framework can claim the crown. https://www.youtube.com/watch?v=H7FCDLqEpk8 https://www.youtube-nocookie.com/embed/H7FCDLqEpk8 yes https://pycon.de/archive/2025/talks/filling-in-the-gaps-when-terraform-falls-short-python-and-typer-step-in/ 2024-12-22 https://pycon.de/static/media/social/talks/CZXBEP.png Filling in the Gaps: When Terraform Falls Short, Python and Typer Step In https://i.ytimg.com/vi/HYc_51nYKQ0/maxresdefault.jpg Filling in the Gaps: When Terraform Falls Short, Python and Typer Step In Not all resources in today’s cloud environments have native Terraform providers. That’s where Python’s Typer library can step in, offering a flexible, production-ready command-line interface (CLI) framework to help fill in the gaps. In this session, we’ll explore how to integrate Typer with Terraform to manage resources that fall outside Terraform’s direct purview. We’ll share a real-life example of how Typer was used alongside Terraform to automate and streamline the management of an otherwise unsupported API. You’ll learn how Terraform can invoke Python scripts—passing arguments and parameters to control complex operations—while still benefiting from Terraform’s declarative model and lifecycle management. We’ll also discuss best practices for defining resource lifecycles to ensure easy maintainability and consistency across deployments. By the end, participants will see how combining Terraform’s robust infrastructure-as-code approach with Python’s versatility and Typer’s user-friendly CLI can create a powerful, cohesive strategy for managing even the trickiest resources in production environments. https://www.youtube.com/watch?v=HYc_51nYKQ0 https://www.youtube-nocookie.com/embed/HYc_51nYKQ0 yes https://pycon.de/archive/2025/talks/forecast-of-hourly-train-counts-on-rail-routes-affected-by-construction-work/ 2024-12-04 https://pycon.de/static/media/social/talks/RAHBEP.png Forecast of Hourly Train Counts on Rail Routes Affected by Construction Work https://i.ytimg.com/vi/gH5T9A_x-Ug/maxresdefault.jpg Forecast of Hourly Train Counts on Rail Routes Affected by Construction Work Construction work in national railroad networks often disrupts train traffic, making it vital to estimate hourly train numbers for effective re-routing. Traditionally managed by humans, this process has been automated due to staff shortages and demographic changes. DB Systel GmbH, Deutsche Bahn's IT provider, leveraged machine learning and artificial intelligence to estimate train traffic during construction. Using Python and frameworks like Pandas, scikit-learn, NumPy, PyTorch and Polars, their solution demonstrated significant benefits in performance and efficiency. https://www.youtube.com/watch?v=gH5T9A_x-Ug https://www.youtube-nocookie.com/embed/gH5T9A_x-Ug yes https://pycon.de/archive/2025/talks/from-algorithm-to-action-building-a-diy-distributed-trading-platform-with-open-source/ 2024-12-14 https://pycon.de/static/media/social/talks/BR3D83.png From Algorithm to Action: Building a DIY Distributed Trading Platform with Open Source https://i.ytimg.com/vi/ZgjI0AV5eKA/maxresdefault.jpg From Algorithm to Action: Building a DIY Distributed Trading Platform with Open Source In this talk, we'll explore how you can implement your own distributed system for algorithmic trading leveraging the power of open source without being dependent on trading bot providers. We will discuss different challenges occurring in HFT inter alia processing massive amounts of data with low latency and reliable risk control and how to solve them. Furthermore we will touch on the topic of regulatory requirements in trading. These challenges will be addressed through a distributed system implemented in Python, utilizing Kafka for real-time data streaming and PostgreSQL for persistent storage. We will examine approaches to decouple the components to re-use and scale them across different markets. Cryptocurrency markets are used as a proving ground for the PoC due to easy availability for everyone. https://www.youtube.com/watch?v=ZgjI0AV5eKA https://www.youtube-nocookie.com/embed/ZgjI0AV5eKA yes https://pycon.de/archive/2025/talks/from-idea-to-integration-an-intro-to-the-model-context-protocol-mcp/ 2025-04-04 https://pycon.de/static/media/social/talks/J7YKEE.png From Idea to Integration: An Intro to the Model Context Protocol (MCP) https://i.ytimg.com/vi/6QiL3qWNtq8/maxresdefault.jpg From Idea to Integration: An Intro to the Model Context Protocol (MCP) The Model Context Protocol (MCP) has emerged as a standard for connecting Large Language Models with diverse data sources and enabling interactions with other systems. In this talk, we’ll introduce the MCP standard and demonstrate how to build a MCP Server using real world examples. We’ll then explore its applications, showing how it empowers developers and makes data from complex systems accessible to non-technical users. Finally, we’ll dive into recent protocol updates, including improvements to Streamable HTTP transport and security enhancements, and share practical strategies for deploying MCP servers as well as clients. https://www.youtube.com/watch?v=6QiL3qWNtq8 https://www.youtube-nocookie.com/embed/6QiL3qWNtq8 yes https://pycon.de/archive/2025/talks/from-like-to-love-adding-proper-search-to-your-django-apps/ 2025-01-03 https://pycon.de/static/media/social/talks/UCG9AS.png From LIKE to Love: Adding Proper Search to Your Django Apps https://i.ytimg.com/vi/zf0gUvYevyE/maxresdefault.jpg From LIKE to Love: Adding Proper Search to Your Django Apps Is your Django application still relying on SQL LIKE queries for search? In this talk, we'll explore why basic text matching falls short of modern user expectations and how to implement proper search functionality without complexity. We'll introduce django-semantic-search, a practical package that bridges the gap between Django's ORM and powerful semantic search capabilities. Through practical code examples and real-world use cases, you'll learn how to enhance your application's search experience from basic keyword matching to understanding user intent. Whether you're building a content platform, e-commerce site, or internal tool, you'll walk away with concrete steps to implement production-ready search that your users will actually enjoy using. https://www.youtube.com/watch?v=zf0gUvYevyE https://www.youtube-nocookie.com/embed/zf0gUvYevyE yes https://pycon.de/archive/2025/talks/from-queries-to-confidence-ensuring-sql-reliability-with-python/ 2024-12-20 https://pycon.de/static/media/social/talks/FSK3PE.png From Queries to Confidence: Ensuring SQL Reliability with Python https://i.ytimg.com/vi/VwoGSrqh364/maxresdefault.jpg From Queries to Confidence: Ensuring SQL Reliability with Python SQL remains a foundational component of data-driven applications, but ensuring the accuracy and reliability of SQL logic is often challenging. SQL testing can be cumbersome, time-consuming, and error-prone. However, these challenges can be addressed by leveraging the simplicity of Python's testing framework such as pytest, enabling clean, robust, and automated SQL testing. https://www.youtube.com/watch?v=VwoGSrqh364 https://www.youtube-nocookie.com/embed/VwoGSrqh364 yes https://pycon.de/archive/2025/talks/from-rules-to-reality-python-s-role-in-shaping-roundnet/ 2024-12-17 https://pycon.de/static/media/social/talks/ZT3MGL.png From Rules to Reality: Python's Role in Shaping Roundnet https://i.ytimg.com/vi/78hdQK0pUtE/maxresdefault.jpg From Rules to Reality: Python's Role in Shaping Roundnet Roundnet is a dynamic and fast-growing sport that combines quick reaction, athleticism, and strong community. However, like many emerging sports, it faces challenges in balancing competition, optimizing rules, and increasing accessibility for both players and spectators. This is where Python and data analysis come into play. In this talk, I'll share insights from my role as Data Lead on the International Roundnet rule committee, where we use Python-powered data analysis to make informed decisions about the future of the sport. We'll explore how analyzing gameplay patterns and testing rule changes with simulation can lead to fairer, more exciting games and attract a broader audience. https://www.youtube.com/watch?v=78hdQK0pUtE https://www.youtube-nocookie.com/embed/78hdQK0pUtE yes https://pycon.de/archive/2025/talks/from-stockouts-to-happy-customers-proven-solutions-for-time-series-forecasting-in-retail/ 2025-01-05 https://pycon.de/static/media/social/talks/QN3BTA.png From stockouts to happy customers: Proven solutions for time series forecasting in retail https://i.ytimg.com/vi/Th4q9oZdA6E/maxresdefault.jpg From stockouts to happy customers: Proven solutions for time series forecasting in retail Time series forecasting in the retail industry is uniquely challenging: Datasets often include stockouts that censor actual demand, promotional events cause irregular demand spikes, new product launches face cold-start issues, and diverse demand patterns within an imbalanced product portfolio create modeling challenges. In this talk, we’ll explore proven, real-world strategies and examples to address these problems. Learn how to successfully handle censored demand caused by stockouts, effectively incorporate promotional effects, and tackle the variability of diverse products using clustering and ensembling strategies. Whether you’re a seasoned data scientist or a Python developer exploring forecasting, the goal of this session is to introduce you to the key challenges in retail forecasting and equip you with actionable insights to successfully overcome them in real-life scenarios. https://www.youtube.com/watch?v=Th4q9oZdA6E https://www.youtube-nocookie.com/embed/Th4q9oZdA6E yes https://pycon.de/archive/2025/talks/from-tensors-to-clouds-a-practical-guide-to-zarr-v3-and-zarr-python-3/ 2024-12-22 https://pycon.de/static/media/social/talks/ABWHSD.png From Tensors to Clouds — A Practical Guide to Zarr V3 and Zarr-Python 3 https://i.ytimg.com/vi/8FX4AXhRNMA/maxresdefault.jpg From Tensors to Clouds — A Practical Guide to Zarr V3 and Zarr-Python 3 A key feature of the Python data ecosystem is the reliance on simple but efficient primitives that follow well-defined interfaces to make tools work seamlessly together (Cf. http://data-apis.org/). NumPy provides an in-memory representation for tensors. Dask provides parallelisation of tensor access. Xarray provides metadata linking tensor dimensions. **Zarr** provides a missing feature, namely the scalable, persistent storage for annotated hierarchies of tensors. Defined through a community process, the Zarr specification enables the storage of large out-of-memory datasets locally and in the cloud. Implementations exist in C++, C, Java, Javascript, Julia, and Python, enabling. This talk presents a systematic approach to understanding and implementing the newer version of [Zarr-Python](https://github.com/zarr-developers/zarr-python), i.e. Zarr-Python 3 by explaining the new API, deprecations, new storage backend, improved codec pipeline, etc. https://www.youtube.com/watch?v=8FX4AXhRNMA https://www.youtube-nocookie.com/embed/8FX4AXhRNMA yes https://pycon.de/archive/2025/talks/from-trees-to-transformers-our-journey-towards-deep-learning-for-ranking/ 2024-12-21 https://pycon.de/static/media/social/talks/83QH37.png From Trees to Transformers: Our Journey Towards Deep Learning for Ranking https://i.ytimg.com/vi/7okPLbQEE1M/maxresdefault.jpg From Trees to Transformers: Our Journey Towards Deep Learning for Ranking GetYourGuide, a global marketplace for travel experiences, reached diminishing returns with its XGBoost-based ranking system. We switched to a Deep Learning pipeline in just nine months, maintaining high throughput and low latency. We iterated on over 50 offline models and conducted more than 10 live A/B tests, ultimately deploying a PyTorch transformer that yielded significant gains. In this talk, we will share our phased approach—from a simple baseline to a high-impact launch—and discuss the key operational and modeling challenges we faced. Learn how to transition from tree-based methods to neural networks and unlock new possibilities for real-time ranking. https://www.youtube.com/watch?v=7okPLbQEE1M https://www.youtube-nocookie.com/embed/7okPLbQEE1M yes https://pycon.de/archive/2025/talks/generative-ai-monitoring-with-pydanticai-and-logfire/ 2025-03-25 https://pycon.de/static/media/social/talks/GS9QWQ.png Generative AI Monitoring with PydanticAI and Logfire https://pycon.de/archive/2025/talks/generative-ai-usecase-specific-evaluation-of-llm-powered-applications/ 2025-03-03 https://pycon.de/static/media/social/talks/GGJDTW.png Generative-AI: Usecase-Specific Evaluation of LLM-powered Applications https://i.ytimg.com/vi/UbKZESDKZN4/maxresdefault.jpg Generative-AI: Usecase-Specific Evaluation of LLM-powered Applications This talk addresses the critical need for usecase-specific evaluation of Large Language Model (LLM)-powered applications, highlighting the limitations of generic evaluation benchmarks in capturing domain-specific requirements. It proposes a workflow for designing more reliable evaluatios to optimize LLM-based applications, consisting of three key activities: human-expert evaluation and benchmark dataset curation, creation of evaluation agents, and alignment of these agents with human evaluations using the curated datasets. The workflow produces two key outcomes: a curated benchmark dataset for testing LLM applications and an evaluation agent that scores their responses. The presentation further addresses the limitations, and best practices to enhance the reliability of evaluations, ensuring LLM applications are better tailored to specific use cases. https://www.youtube.com/watch?v=UbKZESDKZN4 https://www.youtube-nocookie.com/embed/UbKZESDKZN4 yes https://pycon.de/archive/2025/talks/getting-started-with-bayes-in-engineering-implementing-kalman-filters-with-rxinfer-jl/ 2024-12-21 https://pycon.de/static/media/social/talks/U9KHNA.png Getting Started with Bayes in Engineering: Implementing Kalman Filters with RxInfer.jl https://i.ytimg.com/vi/w1EU-B5Gzoo/maxresdefault.jpg Getting Started with Bayes in Engineering: Implementing Kalman Filters with RxInfer.jl Bayesian methods are not commonly seen in Civil Engineering and Structural Dynamics. In this talk we explore how RxInfer.jl and the Julia Programming Language can simplify Bayesian modeling by implementing a Kalman filter for tracking the dynamics of a structural system. Perfect for engineers, researchers, and data scientists eager to apply probabilistic modelling and Bayesian methods to real-world engineering challenges. https://www.youtube.com/watch?v=w1EU-B5Gzoo https://www.youtube-nocookie.com/embed/w1EU-B5Gzoo yes https://pycon.de/archive/2025/talks/gitmlops-how-we-are-managing-100-ml-pipelines-in-aws-sagemaker/ 2024-12-16 https://pycon.de/static/media/social/talks/DPAPUA.png GitMLOps – How we are managing 100+ ML pipelines in AWS SageMaker https://i.ytimg.com/vi/kkEFux6uek8/maxresdefault.jpg GitMLOps – How we are managing 100+ ML pipelines in AWS SageMaker Scaling machine learning pipelines is no small feat - especially when you’re managing over 100 of them on AWS SageMaker. In this talk, I’ll take you behind the scenes of how our team at idealo built a Git-based MLOps framework that powers millions of real-time recommendations every minute. I’ll share the challenges we faced, the solutions we implemented, and the lessons we learned while streamlining model versioning, deployment, and monitoring. This session is packed with actionable takeaways for ML engineers, data scientists, and DevOps professionals looking to simplify their MLOps workflows and operate efficiently at scale. Whether you’re running a handful of pipelines or preparing to scale up, this talk will equip you with the tools and strategies to tackle MLOps with confidence. https://www.youtube.com/watch?v=kkEFux6uek8 https://www.youtube-nocookie.com/embed/kkEFux6uek8 yes https://pycon.de/archive/2025/talks/going-global-taking-code-from-research-to-operational-open-ecosystem-for-ai-weather-forecasting/ 2024-12-23 https://pycon.de/static/media/social/talks/WMBDJ8.png Going Global: Taking code from research to operational open ecosystem for AI weather forecasting https://i.ytimg.com/vi/afnWiypKTIM/maxresdefault.jpg Going Global: Taking code from research to operational open ecosystem for AI weather forecasting When I was hired as a Scientist for Machine Learning, experts said ML would never work in weather forecasting. Nowadays, I get to contribute to Anemoi, a full-featured ML weather forecasting framework used by international weather agencies to research, build, and scale AI weather forecasting models. The project started out as a curiosity by my colleagues and soon scaled as a result of its initial success. As machine learning stories go, this is a story of change, adaptation and making things work. In this talk, I'll share some practical lessons: how we evolved from a mono-package with four people working on it to multiple open-source packages with 40+ internal and external collaborators. Specifically, how we managed the explosion of over 300 config options without losing all of our sanity, building a separation of packages that works for both researchers and operations teams, as well as CI/CD and testing that constrains how many bugs we can introduce in a given day. You'll learn concrete patterns for growing Python packaging for ML systems, and balancing research flexibility with production stability. As a bonus, I'll sprinkle in anecdotes where LLMs like chatGPT and Copilot massively failed at facilitating this evolution. Join me for a deep dive into the real challenges of scaling ML systems - where the weather may be hard to predict, but our code doesn't have to be. https://www.youtube.com/watch?v=afnWiypKTIM https://www.youtube-nocookie.com/embed/afnWiypKTIM yes https://pycon.de/archive/2025/talks/guardians-of-the-code-safeguarding-machine-learning-models-in-a-climate-tech-world/ 2024-12-03 https://pycon.de/static/media/social/talks/CVMPVG.png Guardians of the Code: Safeguarding Machine Learning Models in a Climate Tech World https://i.ytimg.com/vi/FXBqn_bBDf0/maxresdefault.jpg Guardians of the Code: Safeguarding Machine Learning Models in a Climate Tech World LLMs, Machine learning and AI are everywhere, yet their security is often overlooked, leaving your systems vulnerable to serious attacks. What happens when someone tampers with your model’s input, poisons your training data, or steals your model? In this talk, I’ll explore these risks through the lens of the OWASP Machine Learning Security Top 10 using relatable, real-world examples from the climate tech world. I’ll explain how these attacks happen, their impact, and why they matter to you as a Python developer, data scientist, or data engineer. You’ll learn practical ways to defend your models and pipelines, ensuring they’re robust against adversarial forces. Bridging theory and practice, you'll leave equipped with insights and strategies to secure your machine learning systems, whether you’re training models or deploying them in production. By the end, you’ll have a solid understanding of the risks, a toolkit of best practices, and maybe even a new perspective on how important security is everywhere. https://www.youtube.com/watch?v=FXBqn_bBDf0 https://www.youtube-nocookie.com/embed/FXBqn_bBDf0 yes https://pycon.de/archive/2025/talks/guiding-data-minds-how-mentoring-transforms-careers-for-both-sides/ 2024-12-08 https://pycon.de/static/media/social/talks/TYXMZC.png Guiding data minds: how mentoring transforms careers for both sides https://i.ytimg.com/vi/d_ha7WyCVXI/maxresdefault.jpg Guiding data minds: how mentoring transforms careers for both sides Mentorship is a powerful way to shape careers while building meaningful connections in the data field. In this talk, I’ll share my journey as a professional mentor, what the role entails, and the impact it has on both mentees and mentors. Learn how mentorship drives growth, fosters innovation, and creates value for the data community—and why you should consider stepping into this rewarding role. https://www.youtube.com/watch?v=d_ha7WyCVXI https://www.youtube-nocookie.com/embed/d_ha7WyCVXI yes https://pycon.de/archive/2025/talks/hands-on-llm-security-attacks-and-countermeasures-you-need-to-know/ 2024-12-20 https://pycon.de/static/media/social/talks/3DSU8V.png Hands-On LLM Security: Attacks and Countermeasures You Need to Know! https://i.ytimg.com/vi/yhUP1C4tI4Q/maxresdefault.jpg Hands-On LLM Security: Attacks and Countermeasures You Need to Know! Dive into the vulnerabilities of LLMs and learn how to prevent them From prompt injection to data poisoning, we’ll demonstrate real-world attack scenarios and reveal essential countermeasures to safeguard your applications. https://www.youtube.com/watch?v=yhUP1C4tI4Q https://www.youtube-nocookie.com/embed/yhUP1C4tI4Q yes https://pycon.de/archive/2025/talks/high-performance-dataframe-agnostic-glms-with-glum/ 2025-01-04 https://pycon.de/static/media/social/talks/JUQ9JJ.png High-performance dataframe-agnostic GLMs with glum https://i.ytimg.com/vi/pLGoPOGpSUI/maxresdefault.jpg High-performance dataframe-agnostic GLMs with glum Generalized linear models (GLMs) are interpretable, relatively quick to train, and specifying them helps the modeler understand the main effects in the data. This makes them a popular choice today to complement other machine-learning approaches. `glum` was conceived with the aim of offering the community an efficient, feature-rich, and Python-first GLM library with a scikit-learn-style API. More recently, we are striving to keep up with PyData community's ongoing push for dataframe-agnosticism. While `glum` was originally heavily based on `pandas`, with the help of `narwhals`, we are close to being able to fit models on any dataset that the latter supports. This talk presents our experiences with achieving this goal. https://www.youtube.com/watch?v=pLGoPOGpSUI https://www.youtube-nocookie.com/embed/pLGoPOGpSUI yes https://pycon.de/archive/2025/talks/how-narwhals-is-silently-bringing-pandas-polars-duckdb-pyarrow-and-more-together/ 2024-12-20 https://pycon.de/static/media/social/talks/CPCNRZ.png How Narwhals is silently bringing pandas, Polars, DuckDB, PyArrow, and more together https://i.ytimg.com/vi/DJk782DWcss/maxresdefault.jpg How Narwhals is silently bringing pandas, Polars, DuckDB, PyArrow, and more together If you were writing a data science tool in 2015, you'd have ensured it supported pandas and then called it a day. But it's not 2015 anymore, we've fast-forwarded to 2025. If you write a tool which only supports pandas, users will demand support for Polars, PyArrow, DuckDB, and so many other libraries that you'll feel like giving up. Learn about how Narwhals allows you to write dataframe-agnostic tools which can support all of the above, with zero dependencies, low overhead, static typing, and strong backwards-compatibility promises! https://www.youtube.com/watch?v=DJk782DWcss https://www.youtube-nocookie.com/embed/DJk782DWcss yes https://pycon.de/archive/2025/talks/how-to-use-data-science-superpowers-in-real-life-a-bayesian-perspective/ 2024-12-17 https://pycon.de/static/media/social/talks/RLTZTC.png How to use Data Science Superpowers in real life, a Bayesian perspective https://pycon.de/archive/2025/talks/inclusive-data-for-1-3-billion-designing-accessible-visualizations/ 2024-12-22 https://pycon.de/static/media/social/talks/LNW3KE.png Inclusive Data for 1.3 Billion: Designing Accessible Visualizations https://i.ytimg.com/vi/UP-sLRPbQYo/maxresdefault.jpg Inclusive Data for 1.3 Billion: Designing Accessible Visualizations According to the World Health Organization (WHO), an estimated 1.3 billion people (1 in 6 individuals) experience a disability, and nearly 2.2 billion people (1 in 5 individuals) have vision impairment. Improving the accessibility of visualizations will enable more people to participate in and engage with our data analyses. In this talk, we’ll discuss some principles and best practices for creating more accessible data visualizations. It will include tips for individuals who create visualizations, as well as guidelines for the developers of visualization software to help ensure your tools can help downstream designers and developers create more accessible visualizations. https://www.youtube.com/watch?v=UP-sLRPbQYo https://www.youtube-nocookie.com/embed/UP-sLRPbQYo yes https://pycon.de/archive/2025/talks/information-retrieval-without-feeling-lucky-the-art-and-science-of-search/ 2025-01-05 https://pycon.de/static/media/social/talks/ZHT9HW.png Information Retrieval Without Feeling Lucky: The Art and Science of Search https://pycon.de/archive/2025/talks/instrumenting-python-applications-with-opentelemetry/ 2025-01-05 https://pycon.de/static/media/social/talks/UH7FXA.png Instrumenting Python Applications with OpenTelemetry https://i.ytimg.com/vi/X-cIxrKyhQA/maxresdefault.jpg Instrumenting Python Applications with OpenTelemetry Observability is challenging and often requires vendor-specific instrumentation. Enter OpenTelemetry: a vendor-agnostic standard for logs, metrics, and traces. Learn how to instrument Python applications with OpenTelemetry and send telemetry to your preferred observability backends. https://www.youtube.com/watch?v=X-cIxrKyhQA https://www.youtube-nocookie.com/embed/X-cIxrKyhQA yes https://pycon.de/archive/2025/talks/interactive-end-to-end-root-cause-analysis-with-explainable-ai-in-a-python-shiny-app/ 2025-03-05 https://pycon.de/static/media/social/talks/AGLBMF.png Interactive end-to-end root-cause analysis with explainable AI in a Python Shiny App https://pycon.de/archive/2025/talks/introducing-the-synthetic-data-sdk-privacy-preserving-synthetic-data-for-ai-ml/ 2025-03-04 https://pycon.de/static/media/social/talks/MQG9HN.png Introducing the Synthetic Data SDK - Privacy Preserving Synthetic Data for AI/ML https://pycon.de/archive/2025/talks/intuitive-a-b-test-evaluations-for-coders/ 2024-12-21 https://pycon.de/static/media/social/talks/RQ8JBM.png Intuitive A/B Test Evaluations for Coders https://i.ytimg.com/vi/KAopX344WGM/maxresdefault.jpg Intuitive A/B Test Evaluations for Coders A/B testing is a critical tool for making data-driven decisions, yet its statistical underpinnings—p-values, confidence intervals, and hypothesis testing—are often challenging for those without a background in statistics. Coders frequently encounter these concepts but lack a straightforward way to compute and interpret them using their existing skill set. This talk presents a practical approach to A/B test evaluations tailored for coders. By utilizing Python’s random number generator and basic loops, it introduces bootstrapping as an accessible method for calculating p-values and confidence intervals directly from data. The goal is to simplify statistical concepts and provide coders with an intuitive understanding of how to evaluate test results without relying on complex formulas or statistical jargon. https://www.youtube.com/watch?v=KAopX344WGM https://www.youtube-nocookie.com/embed/KAopX344WGM yes https://pycon.de/archive/2025/talks/is-prompt-engineering-dead-how-auto-optimization-is-changing-the-game/ 2024-12-20 https://pycon.de/static/media/social/talks/GURXPK.png Is Prompt Engineering Dead? How Auto-Optimization is Changing the Game https://i.ytimg.com/vi/3meErI1Qlag/maxresdefault.jpg Is Prompt Engineering Dead? How Auto-Optimization is Changing the Game The rise of LLMs has elevated prompt engineering as a critical skill in the AI industry, but manual prompt tuning is often inefficient and model-specific. This talk explores various automatic prompt optimization approaches, ranging from simple ones like bootstrapped few-shot to more complex techniques such as MIPRO and TextGrad, and showcases their practical applications through frameworks like DSPy and AdalFlow. By exploring the benefits, challenges, and trade-offs of these approaches, the attendees will be able to answer the question: is prompt engineering dead, or has it just evolved? https://www.youtube.com/watch?v=3meErI1Qlag https://www.youtube-nocookie.com/embed/3meErI1Qlag yes https://pycon.de/archive/2025/talks/is-your-llm-any-good-at-writing-benchmarking-on-creative-writing-and-editing-tasks/ 2025-01-05 https://pycon.de/static/media/social/talks/P8GUWG.png Is your LLM any good at writing? Benchmarking on creative writing and editing tasks https://i.ytimg.com/vi/pZ3bdLW0e-A/maxresdefault.jpg Is your LLM any good at writing? Benchmarking on creative writing and editing tasks Many LLM benchmarks focus on reasoning and coding tasks. These are exciting tasks! But the majority of LLM usage is still in writing and editing related tasks, and there's a surprising lack of benchmarks on these. In this talk you'll learn what it took to create a writing benchmark, and which model performs best! https://www.youtube.com/watch?v=pZ3bdLW0e-A https://www.youtube-nocookie.com/embed/pZ3bdLW0e-A yes https://pycon.de/archive/2025/talks/jeannie-an-agentic-field-worker-assistant/ 2025-03-13 https://pycon.de/static/media/social/talks/7CAVX7.png Jeannie: An Agentic Field Worker Assistant https://i.ytimg.com/vi/HppVxUdO6Hk/maxresdefault.jpg Jeannie: An Agentic Field Worker Assistant Jeannie is an LLM-based agentic workflow implemented in Python to automate task management for field workers in the energy sector. This system addresses inefficiencies and safety risks in tasks like PV panel installation and powerline repair. Using open-source tools (LangChain family, OpenStreetMap and OpenWeatherMap APIs), Jeannie retrieves tasks, fetches weather and directions, identifies past incidents via RAG, and emails tailored reports with safety warnings. This presentation offers a case study of Jeannie’s implementation for E.ON in Germany, demonstrating how daily task automation enhances worker safety and efficiency. Attendees will discover how to create agentic systems with Python, integrate APIs, and apply RAG for safety applications, with access to open-source code and data for replicating the workflow. https://www.youtube.com/watch?v=HppVxUdO6Hk https://www.youtube-nocookie.com/embed/HppVxUdO6Hk yes https://pycon.de/archive/2025/talks/langfuse-openlit-and-phoenix-observability-for-the-genai-era/ 2024-12-22 https://pycon.de/static/media/social/talks/HKYQDB.png Langfuse, OpenLIT, and Phoenix: Observability for the GenAI Era https://i.ytimg.com/vi/EE3xRZYRyyQ/maxresdefault.jpg Langfuse, OpenLIT, and Phoenix: Observability for the GenAI Era Large Language Models (LLMs) are transforming digital products, but their non-deterministic behaviour challenges predictability and testing, making observability essential for quality and scalability. This talk presents **observability for LLM-based applications**, spotlighting three tools: Langfuse, OpenLIT, and Phoenix. We'll share best practices about what and how to monitor LLM features and explore each tool's strengths and limitations. Langfuse excels in tracing and quality monitoring but lacks OpenTelemetry support and customization. OpenLIT, while less mature, integrates well with existing observability stacks using **OpenTelemetry**. Phoenix stands out in debugging and experimentation but struggles with real-time tracing. The comparison will be enhanced by **live coding examples**. Attendees will walk away with an improved understanding of observability for **GenAI applications** and will understand which tool to use for their use case. https://www.youtube.com/watch?v=EE3xRZYRyyQ https://www.youtube-nocookie.com/embed/EE3xRZYRyyQ yes https://pycon.de/archive/2025/talks/learnings-from-migrating-a-flask-app-to-fastapi/ 2024-12-19 https://pycon.de/static/media/social/talks/EDJ8N7.png Learnings from migrating a Flask app to FastAPI https://i.ytimg.com/vi/JxyUKL8jhBM/maxresdefault.jpg Learnings from migrating a Flask app to FastAPI FastAPI has been constantly growing in popularity during the last years. A lot of this growth is driven by its relative simplicity and ease-of-use. In this talk, we'll discuss some practical insights into building a FastAPI application, based on my experience of migrating an existing Flask prototype to FastAPI. We'll explore how FastAPI's core features like Pydantic integration and dependency injection can improve API development, while also talking about the drawbacks of FastAPI. https://www.youtube.com/watch?v=JxyUKL8jhBM https://www.youtube-nocookie.com/embed/JxyUKL8jhBM yes https://pycon.de/archive/2025/talks/lessons-learned-in-bringing-a-rag-chatbot-with-access-to-50k-diverse-documents-to-production/ 2024-12-20 https://pycon.de/static/media/social/talks/XLZQFA.png Lessons learned in bringing a RAG chatbot with access to 50k+ diverse documents to production https://i.ytimg.com/vi/7IZqnDX2HRk/maxresdefault.jpg Lessons learned in bringing a RAG chatbot with access to 50k+ diverse documents to production Retrieval-Augmented Generation (RAG) chatbots are a key use case of GenAI in organizations, allowing users to conveniently access and query internal company data. A first RAG prototype can often be created in a matter of days. But why are the majority of prototypes still in the pilot stage? [\[1\]](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-state-of-gen-ai-q3.pdf) In this talk we share our insights from developing a production-grade chatbot at Merck. Our RAG chatbot for R&D experts accesses over 50,000 documents across numerous SharePoint sites and other sources. We identified three technical key success factors: 1. Building a robust data pipeline that syncs documents from source systems and that handles enterprise features such as replicating user permissions. 2. Developing a chatbot workflow from user question to answer with retrieval components such as hybrid search and reranking 3. Establishing a comprehensive evaluation framework with a clear optimization metric. We think that many of these lessons are broadly applicable to RAG chatbots, making this talk valuable for practitioners aiming to implement GenAI solutions in business contexts. https://www.youtube.com/watch?v=7IZqnDX2HRk https://www.youtube-nocookie.com/embed/7IZqnDX2HRk yes https://pycon.de/archive/2025/talks/lightning-talks-1-2/ 2025-04-12 https://pycon.de/static/media/social/talks/ESD7KF.png Lightning Talks (1/2) https://i.ytimg.com/vi/SaYC1QIn18w/maxresdefault.jpg Lightning Talks (1/2) Lightning Talks at PyCon DE & PyData are short, 5-minute presentations open to all attendees. They’re a fun and fast-paced way to share ideas, showcase projects, spark discussions, or raise awareness about topics you care about — whether technical, community-related, or just inspiring. No slides are required, and talks can be spontaneous or prepared. It’s a great chance to speak up and connect with the community! Please note: community conference and event announcements are limited to 1 minute only. All event announcements will be collected in a slide slide deck. https://www.youtube.com/watch?v=SaYC1QIn18w https://www.youtube-nocookie.com/embed/SaYC1QIn18w yes https://pycon.de/archive/2025/talks/lightning-talks-2-2/ 2025-04-12 https://pycon.de/static/media/social/talks/SUDMDV.png Lightning Talks (2/2) https://i.ytimg.com/vi/p5ERIJvz0rA/maxresdefault.jpg Lightning Talks (2/2) Lightning Talks at PyCon DE & PyData are short, 5-minute presentations open to all attendees. They’re a fun and fast-paced way to share ideas, showcase projects, spark discussions, or raise awareness about topics you care about — whether technical, community-related, or just inspiring. No slides are required, and talks can be spontaneous or prepared. It’s a great chance to speak up and connect with the community! Please note: community conference and event announcements are limited to 1 minute only. All event announcements will be collected in a slide slide deck. https://www.youtube.com/watch?v=p5ERIJvz0rA https://www.youtube-nocookie.com/embed/p5ERIJvz0rA yes https://pycon.de/archive/2025/talks/llm-inference-arithmetics-the-theory-behind-model-serving/ 2024-12-22 https://pycon.de/static/media/social/talks/G3AT7E.png LLM Inference Arithmetics: the Theory behind Model Serving https://i.ytimg.com/vi/y3tag45TX9M/maxresdefault.jpg LLM Inference Arithmetics: the Theory behind Model Serving Have you ever asked yourself how parameters for an LLM are counted, or wondered why Gemma 2B is actually closer to a 3B model? You have no clue about what a KV-Cache is? (And, before you ask: no, it's not a Redis fork.) Do you want to find out how much GPU VRAM you need to run your model smoothly? If your answer to any of these questions was "yes", or you have another doubt about inference with LLMs - such as batching, or time-to-first-token - this talk is for you. Well, except for the Redis part. https://www.youtube.com/watch?v=y3tag45TX9M https://www.youtube-nocookie.com/embed/y3tag45TX9M yes https://pycon.de/archive/2025/talks/machine-learning-models-in-a-dynamic-environment/ 2025-02-02 https://pycon.de/static/media/social/talks/3FUYVH.png Machine Learning Models in a Dynamic Environment https://i.ytimg.com/vi/DYpjKY0GjM0/maxresdefault.jpg Machine Learning Models in a Dynamic Environment "We've only tested the happy path - now users are finding all sorts of creative ways to break the app." What is already a cause for headaches in traditional software engineering turns into a large challenge when the application is based on machine learning models: Data distribution may change from training phase to deployment. Even worse, humans interacting with the model may adjust their behaviour to the model making the gap between original training environment and deployment even larger. When deployed in a public environment the model may be exposed to users trying to game the system. When re-trained it may be exposed to users trying to poison the pool of training data. We will take a tour of historic cases of models being gamed: What are the lessons we learnt a long time ago building e-mail spam filters? What happened when high search engine rankings started to be linked to monetary income? How can personalization and targeted advertising be exploited to influence public discourse? “… it should be clear that improvements in communication tend to divide mankind …” by Harold Innis in Changing Concepts of Time This keynote will turn interactive engaging the audience in sharing their stories on users playing interesting games with deployed models - including counter moves rolled out. If we are to learn from IT security experience, one important ingredient to address these issues is a combination of collaboration and transparency - across organisations. https://www.youtube.com/watch?v=DYpjKY0GjM0 https://www.youtube-nocookie.com/embed/DYpjKY0GjM0 yes https://pycon.de/archive/2025/talks/machine-reasoning-and-system-2-thinking/ 2024-12-22 https://pycon.de/static/media/social/talks/AYN837.png Machine Reasoning and System 2 Thinking https://i.ytimg.com/vi/jnKLgqk4Wro/maxresdefault.jpg Machine Reasoning and System 2 Thinking Raw large language models struggle with complex reasoning. New techniques have emerged that allow these models to spend more time thinking before giving an answer. Direct token sampling can be seen as system-1 thinking and explicit step-by-step reasoning as system-2. How can this reasoning ability be improved and what is the future? https://www.youtube.com/watch?v=jnKLgqk4Wro https://www.youtube-nocookie.com/embed/jnKLgqk4Wro yes https://pycon.de/archive/2025/talks/mastering-demand-forecasting-lessons-from-europe-s-largest-retailer/ 2024-12-21 https://pycon.de/static/media/social/talks/NNGWGC.png Mastering Demand Forecasting: Lessons from Europe's Largest Retailer https://i.ytimg.com/vi/7DjU7pTawKQ/maxresdefault.jpg Mastering Demand Forecasting: Lessons from Europe's Largest Retailer Ever craved your favorite dish, only to find its key ingredient missing from the store? You're not alone - stock outs can have significant consequences for businesses, resulting in frustrated customers and lost sales. On the other hand, overstocking can lead to wasted storage costs and potential write-offs. The replenishment system is responsible for striking the right balance between these opposing risks. The key to successful replenishment is making accurate predictions about future demand. This presentation takes a deep dive into the intricate world of demand forecasting, at Europe's largest retailer. We will demonstrate how enhancing simple machine learning methods with domain knowledge allows to generate hundreds of millions of high-quality forecasts every day. https://www.youtube.com/watch?v=7DjU7pTawKQ https://www.youtube-nocookie.com/embed/7DjU7pTawKQ yes https://pycon.de/archive/2025/talks/mini-pythonistas-coding-experimenting-and-exploring-with-zumi/ 2025-02-03 https://pycon.de/static/media/social/talks/VC3T39.png Mini-Pythonistas: Coding, Experimenting, and Exploring with Zümi! https://pycon.de/archive/2025/talks/modern-nlp-for-proactive-harmful-content-moderation/ 2024-12-20 https://pycon.de/static/media/social/talks/F9EFXA.png Modern NLP for Proactive Harmful Content Moderation https://i.ytimg.com/vi/nmxfp-CxoXc/maxresdefault.jpg Modern NLP for Proactive Harmful Content Moderation Despite an array of regulations implemented by governments and social media platforms worldwide (i.e. famous DSA), the problem of digital abusive speech persists. At the same time, rapid advances in NLP and large language models (LLMs) are opening up new possibilities—and responsibilities—for using this technology to make a positive social impact. Can LLMs streamline content moderation efforts? Are they effective at spotting and countering hate speech, and can they help produce more proactive solutions like text detoxification and counter-speech generation? In this talk, we will dive into the cutting-edge research and best practices of automatic textual content moderation today. From clarifying core definitions to detailing actionable methods for leveraging multilingual NLP models, we will provide a practical roadmap for researchers, developers, and policymakers aiming to tackle the challenges of harmful online content. Join us to discover how modern NLP can foster safer, more inclusive digital communities. https://www.youtube.com/watch?v=nmxfp-CxoXc https://www.youtube-nocookie.com/embed/nmxfp-CxoXc yes https://pycon.de/archive/2025/talks/multi-tenant-conversational-analytics/ 2025-01-03 https://pycon.de/static/media/social/talks/KCSSJ7.png Multi-tenant Conversational Analytics https://i.ytimg.com/vi/HHr7AGN2xAA/maxresdefault.jpg Multi-tenant Conversational Analytics Ever wondered how to use GenAI to enable self-service analytics through prompting? In this talk, I will share my experience of building a multi-tenant conversational analytics set-up that is built into a Software-as-a-Service (SaaS) platform. This talk is intended for AI engineers, data scientists, software engineers and anyone interested in using GenAI to power conversational analytics using open-source tools. I will discuss the challenges faced in designing and implementing, as well as the lessons learned along the way. We'll answer questions such as, why offer analytics through prompting? Why multi-tenancy and makes it so difficult? How to build it into an existing product? What makes open-source the preferred choice over proprietary solutions? What could the implications be for the analytics field? https://www.youtube.com/watch?v=HHr7AGN2xAA https://www.youtube-nocookie.com/embed/HHr7AGN2xAA yes https://pycon.de/archive/2025/talks/navigating-the-security-maze-an-interactive-adventure/ 2024-12-20 https://pycon.de/static/media/social/talks/3WLDMQ.png Navigating the Security Maze: An Interactive Adventure https://i.ytimg.com/vi/--WDDsBCU4w/maxresdefault.jpg Navigating the Security Maze: An Interactive Adventure How to integrate security into a software development project? Without jeopardizing timeline or budget? You decide! This interactive session covers crucial decisions for software security, and the audience decides how the story ends... https://www.youtube.com/watch?v=--WDDsBCU4w https://www.youtube-nocookie.com/embed/--WDDsBCU4w yes https://pycon.de/archive/2025/talks/offline-disaster-relief-coordination-with-openstreetmap-and-fastapi/ 2024-12-10 https://pycon.de/static/media/social/talks/GUEAHT.png Offline Disaster Relief Coordination with OpenStreetMap and FastAPI https://i.ytimg.com/vi/2USI-Eaecn0/maxresdefault.jpg Offline Disaster Relief Coordination with OpenStreetMap and FastAPI In natural disaster scenarios, reliable communication is crucial. This talk presents a solution for disaster relief coordination using OpenStreetMap vector maps hosted on a local device in the emergency vehicle with FastAPI, ensuring functionality without an internet connection. By integrating a database of post codes and street names, and leveraging a LORAWAN gateway to receive positional data and water levels, this system ensures access to critical information even in blackout situations. https://www.youtube.com/watch?v=2USI-Eaecn0 https://www.youtube-nocookie.com/embed/2USI-Eaecn0 yes https://pycon.de/archive/2025/talks/oh-my-license-achieving-order-by-automation-in-the-license-chaos-of-your-dependencies/ 2024-12-21 https://pycon.de/static/media/social/talks/ME7XPJ.png Oh my license! – Achieving order by automation in the license chaos of your dependencies https://i.ytimg.com/vi/rdzTOw2Z13Y/maxresdefault.jpg Oh my license! – Achieving order by automation in the license chaos of your dependencies License issues can haunt you at night. You spend days, weeks, and months developing beautiful software. But then it happens. You realize that an essential dependency is GPL-3.0 licensed. All your code is now infected with this license. Now you are forced to either: 1. Rewrite all parts relying on the other library 2. Open-source your codebase under the GPL-3.0 license How could this have been avoided? Join the talk and find out! First, we’ll give you a brief introduction to different software licenses and their implications. Second, we’ll show you how to automate your license checking using open-source software. https://www.youtube.com/watch?v=rdzTOw2Z13Y https://www.youtube-nocookie.com/embed/rdzTOw2Z13Y yes https://pycon.de/archive/2025/talks/oh-no-users-love-my-genai-prototype-and-want-to-use-it-more/ 2025-01-02 https://pycon.de/static/media/social/talks/UXTCZC.png Oh, no! Users love my GenAI-Prototype and want to use it more. https://i.ytimg.com/vi/BR_zakAFO0c/maxresdefault.jpg Oh, no! Users love my GenAI-Prototype and want to use it more. Demos and prototypes for generative AI (GenAI) projects can be quickly created with tools like Streamlit, offering impressive results for users within hours. However, scaling these solutions from prototypes to robust systems introduces significant challenges. As user demand grows, hacks and workarounds in tools like Streamlit lead to unreliability and debugging frustrations. This talk explores the journey of overcoming these obstacles, evolving to a stable tech stack with Qdrant, Postgres, Litellm, FastAPI, and Streamlit. Aimed at beginners in GenAI, it highlights key lessons. https://www.youtube.com/watch?v=BR_zakAFO0c https://www.youtube-nocookie.com/embed/BR_zakAFO0c yes https://pycon.de/archive/2025/talks/open-table-formats-in-the-wild-from-parquet-to-delta-lake-and-back/ 2024-12-19 https://pycon.de/static/media/social/talks/MRHNCV.png Open Table Formats in the Wild: From Parquet to Delta Lake and Back https://i.ytimg.com/vi/YdFeHj5lRP4/maxresdefault.jpg Open Table Formats in the Wild: From Parquet to Delta Lake and Back Open table formats have revolutionized analytical, columnar storage on cloud object stores with critical features like ACID compliance and enhanced metadata management, once exclusive to proprietary cloud data warehouses. Delta Lake, Iceberg, and Hudi have significantly advanced over traditional open file formats like Parquet and ORC. In an effort to modernize our data architecture, we aimed to replace our Parquet-based bronze layer with Delta Lake, anticipating better query performance, reduced maintenance, native support for incremental processing, and more. While our initial pilot showed promise, we encountered unexpected pitfalls that ultimately brought us back to where we began. Curious? Join me as we shed light on the current state of table formats. https://www.youtube.com/watch?v=YdFeHj5lRP4 https://www.youtube-nocookie.com/embed/YdFeHj5lRP4 yes https://pycon.de/archive/2025/talks/optimizing-energy-tariffing-system-with-formal-concept-analysis-and-dash/ 2024-12-19 https://pycon.de/static/media/social/talks/B8TUR9.png Optimizing Energy Tariffing System with Formal Concept Analysis and Dash https://i.ytimg.com/vi/4XdyHszFRME/maxresdefault.jpg Optimizing Energy Tariffing System with Formal Concept Analysis and Dash As a data scientist, I value the power of insightful visualizations to unlock unique interpretations of complex data. In my talk, I will introduce an elegant mathematical framework called Formal Concept Analysis (FCA), developed in the 1980s in Darmstadt. FCA transforms binary data into concepts that can be visualized as a hierarchical graph, offering a fresh perspective on multidimensional data analysis. Leveraging this theory and its open-source Python libraries, I am developing an interactive Dash-based tool featuring interactive tables and graphs to explore data insights. To illustrate its potential, I will showcase an optimization of the entire tariffing system of an energy provider company, highlighting how FCA can bring structure and clarity to even such tangled datasets. https://www.youtube.com/watch?v=4XdyHszFRME https://www.youtube-nocookie.com/embed/4XdyHszFRME yes https://pycon.de/archive/2025/talks/optimizing-in-the-python-ecosystem-powered-by-gurobi/ 2025-03-14 https://pycon.de/static/media/social/talks/BAASYV.png Optimizing in the Python Ecosystem – Powered by Gurobi https://i.ytimg.com/vi/1LZqimczFKw/maxresdefault.jpg Optimizing in the Python Ecosystem – Powered by Gurobi Join us as we explore integrating Gurobi and prescriptive analytics into your Python ecosystem. In this session, you’ll discover model-building techniques that leverage NumPy and SciPy.sparse as well as the data structures of pandas. We’ll also show you how to seamlessly integrate trained regressors from scikit-learn as constraints in your optimization models. Elevate your workflows and unlock new decision-making capabilities with Gurobi in Python. https://www.youtube.com/watch?v=1LZqimczFKw https://www.youtube-nocookie.com/embed/1LZqimczFKw yes https://pycon.de/archive/2025/talks/outgrowing-your-node-zero-stress-scaling-with-cupynumeric/ 2024-12-20 https://pycon.de/static/media/social/talks/HPGEKH.png Outgrowing your node? Zero stress scaling with cuPyNumeric. https://i.ytimg.com/vi/zqJrImbKHpY/maxresdefault.jpg Outgrowing your node? Zero stress scaling with cuPyNumeric. Many data and simulation scientists use NumPy for its ease of use and good performance on CPU. This approach works well for single-node tasks, but scaling to handle larger datasets or more resource-intensive computations introduces significant challenges. Not to mention, using GPUs requires another level of complexity. We present the cuPyNumeric library, which gives developers the same familiar NumPy interface, but seamlessly distributes work across CPUs and GPUs. In this talk we showcase the productivity and performance of cuPyNumeric library on one of the user's examples covering some detail on its implementation. https://www.youtube.com/watch?v=zqJrImbKHpY https://www.youtube-nocookie.com/embed/zqJrImbKHpY yes https://pycon.de/archive/2025/talks/pdfs-when-a-thousand-words-are-worth-more-than-a-picture-or-table/ 2024-12-13 https://pycon.de/static/media/social/talks/UVPALT.png PDFs - When a thousand words are worth more than a picture (or table). https://i.ytimg.com/vi/UywLYyNcA8k/maxresdefault.jpg PDFs - When a thousand words are worth more than a picture (or table). PDF, a must-have in RAG systems, ensures visual fidelity across platforms and devices, at the expense of compromising what would be the core condition for computers to properly process and interpret text: semantics. That means any logical arrangement of text, upon rendering, explodes into dummy visual shards of data that literally portrait the bigger picture for the human eye to perceive, but no longer convey the information computers should grasp. Such a bottleneck already makes proper ingestion of text-only documents a big challenge, let alone when tables or figures come into play, the ultimate nightmare for PDF parsers, not to say developers. The rest you must have already foreseen: a RAG system barfing unreliable knowledge from bad chunks (based on regular PDF parsing), if those ever get to be retrieved from a vector database. In this talk you can gather some vision-driven insights on how to leverage the strengths of PDF and language models towards good chunks to be ingested. Or, in other words, how multimodal models can go beyond trivial reverse engineering by decomposing tables into its building blocks, in plain language, as how those would be explained to another human; or better yet, as how humans would ask questions about such pieces of knowledge. And from such a strategy, we transfer the same rationale to figures. Come along, gather some insights, and get inspired to break down tables and figures from your own PDFs, and to improve retrieval in your RAG systems. https://www.youtube.com/watch?v=UywLYyNcA8k https://www.youtube-nocookie.com/embed/UywLYyNcA8k yes https://pycon.de/archive/2025/talks/pipeline-level-differentiable-programming-for-the-real-world/ 2024-12-20 https://pycon.de/static/media/social/talks/JH97CL.png Pipeline-level differentiable programming for the real world https://i.ytimg.com/vi/74cW4HfZ5p8/maxresdefault.jpg Pipeline-level differentiable programming for the real world Automatic Differentiation (AD) is not only the backbone of modern deep learning but also a transformative tool across various domains such as control systems, materials science, weather prediction, 3D rendering, data-driven scientific discovery, and so on. Thanks to a mature ML framework ecosystem, powered by libraries like PyTorch and JAX, AD performs remarkably well at a component level; however, integrating these components into differentiable pipelines still remains a significant challenge. In this talk, we will provide an accessible introduction to (pipeline-level) AD, demonstrate some cool applications you can build with it, and see how to build differentiable pipelines that hold up in the real world. https://www.youtube.com/watch?v=74cW4HfZ5p8 https://www.youtube-nocookie.com/embed/74cW4HfZ5p8 yes https://pycon.de/archive/2025/talks/posepie-replace-your-keyboard-and-mouse-with-ai-driven-gesture-control/ 2024-12-20 https://pycon.de/static/media/social/talks/AEUZGX.png PosePIE: Replace Your Keyboard and Mouse With AI-Driven Gesture Control https://i.ytimg.com/vi/cGwKZnxL_Nc/maxresdefault.jpg PosePIE: Replace Your Keyboard and Mouse With AI-Driven Gesture Control In this talk, we show how to leverage publicly available tools to control any game or program using hand or body movements. To achieve this, we introduce PosePIE, an open-source programmable input emulator that generates input events on virtual gamepads, keyboards and mice based on gestures recognized by using AI-driven pose estimation. PosePIE is fully configurable by the user through Python scripts, making it easily adaptable to new applications. https://www.youtube.com/watch?v=cGwKZnxL_Nc https://www.youtube-nocookie.com/embed/cGwKZnxL_Nc yes https://pycon.de/archive/2025/talks/power-up-your-polars-code-with-polars-extention/ 2024-12-17 https://pycon.de/static/media/social/talks/CP3TKB.png Power up your Polars code with Polars extention https://i.ytimg.com/vi/xKm1TYIO7Iw/maxresdefault.jpg Power up your Polars code with Polars extention While Polars is written in Rust and has the advantages of speed and multi-threaded functionalities., everything will slow down if a Python function needs to be applied to the DataFrame. To avoid that, a Polar extension can be used to solve the problem. In this workshop, we will look at how to do it. https://www.youtube.com/watch?v=xKm1TYIO7Iw https://www.youtube-nocookie.com/embed/xKm1TYIO7Iw yes https://pycon.de/archive/2025/talks/practical-python-rust-building-and-maintaining-dual-language-libraries/ 2025-01-05 https://pycon.de/static/media/social/talks/CUJMCD.png Practical Python/Rust: Building and Maintaining Dual-Language Libraries https://i.ytimg.com/vi/_BtTtpaPbOM/maxresdefault.jpg Practical Python/Rust: Building and Maintaining Dual-Language Libraries Building performant Python often means reaching for C extensions. This talk explores an alternative: leveraging Rust to create blazing-fast Python modules that also benefit the Rust ecosystem. I will share practical strategies from building `semantic-text-splitter`, a library for fast and accurate text segmentation used in both Python and Rust, demonstrating how to bridge the gap between these two languages and unlock new possibilities for performance and cross-language collaboration. https://www.youtube.com/watch?v=_BtTtpaPbOM https://www.youtube-nocookie.com/embed/_BtTtpaPbOM yes https://pycon.de/archive/2025/talks/probably-fun-board-games-to-teach-data-science/ 2024-12-22 https://pycon.de/static/media/social/talks/WELCVS.png Probably Fun: Board Games to teach Data Science https://pycon.de/archive/2025/talks/pydata-stack-pure-python-open-source-data-platforms/ 2025-01-03 https://pycon.de/static/media/social/talks/PRRPQ3.png PyData Stack: Pure Python open source data platforms https://pycon.de/archive/2025/talks/pyladies-panel-ai-skills-careers/ 2025-04-15 https://pycon.de/static/media/social/talks/9TRFCK.png PyLadies Panel: AI Skills & Careers https://i.ytimg.com/vi/DnXRt8u0zvk/maxresdefault.jpg PyLadies Panel: AI Skills & Careers As generative AI and autonomous agents rapidly transform the workplace, the skills required to thrive are evolving just as quickly. This panel will explore the essential AI skills that are driving career growth. https://www.youtube.com/watch?v=DnXRt8u0zvk https://www.youtube-nocookie.com/embed/DnXRt8u0zvk yes https://pycon.de/archive/2025/talks/pytest-simple-rapid-and-fun-testing-with-python/ 2024-12-01 https://pycon.de/static/media/social/talks/PDBAXQ.png pytest - simple, rapid and fun testing with Python https://i.ytimg.com/vi/h3zlTirlwp4/maxresdefault.jpg pytest - simple, rapid and fun testing with Python The pytest tool offers a rapid and simple way to write tests for your Python code. This training gives an introduction with exercises to some distinguishing features, such as its assertions, marks and fixtures. Despite its simplicity, pytest is incredibly flexible and configurable. We'll look at various configuration options as well as the plugin ecosystem around pytest. https://www.youtube.com/watch?v=h3zlTirlwp4 https://www.youtube-nocookie.com/embed/h3zlTirlwp4 yes https://pycon.de/archive/2025/talks/python-performance-unleashed-essential-optimization-techniques-beyond-libraries/ 2024-12-22 https://pycon.de/static/media/social/talks/AJDYRL.png Python Performance Unleashed: Essential Optimization Techniques Beyond Libraries https://i.ytimg.com/vi/qeI8ehsyeY4/maxresdefault.jpg Python Performance Unleashed: Essential Optimization Techniques Beyond Libraries Every Python developer faces performance challenges, from slow data processing to memory-intensive operations. While external libraries like Numba or Cython offer solutions, understanding core Python optimization techniques is crucial for writing efficient code. This talk explores practical optimization strategies using Python's built-in capabilities, demonstrating how to achieve significant performance improvements without external dependencies. Through real-world examples from machine learning pipelines and data processing applications, we'll examine common bottlenecks and their solutions. Whether you're building data pipelines, web applications, or ML systems, these techniques will help you write faster, more efficient Python code. https://www.youtube.com/watch?v=qeI8ehsyeY4 https://www.youtube-nocookie.com/embed/qeI8ehsyeY4 yes https://pycon.de/archive/2025/talks/quiet-on-set-building-an-on-air-sign-with-open-source-technologies/ 2024-12-10 https://pycon.de/static/media/social/talks/P9GRZU.png Quiet on Set: Building an On-Air Sign with Open Source Technologies https://i.ytimg.com/vi/Mw4-nlAhH40/maxresdefault.jpg Quiet on Set: Building an On-Air Sign with Open Source Technologies Learn how to build a custom On-Air sign using Apache Kafka®, Apache Flink®, and Apache Iceberg™! See how to capture events like Zoom meetings and camera usage with Python, process data with FlinkSQL, analyze trends using Iceberg, and bring it all together with a practical IoT project that easily scales out. https://www.youtube.com/watch?v=Mw4-nlAhH40 https://www.youtube-nocookie.com/embed/Mw4-nlAhH40 yes https://pycon.de/archive/2025/talks/reasonable-ai/ 2025-02-02 https://pycon.de/static/media/social/talks/3MNGN8.png Reasonable AI https://i.ytimg.com/vi/r8qK7HYq0uE/maxresdefault.jpg Reasonable AI The relationship between humans and machines, especially in the context of Artificial Intelligence (AI), is shaped by hopes, concerns, and moral questions. On the one hand, advances in AI offer great promise: it can help us solve complex problems, improve healthcare, streamline workflows, and much more. Yet, at the same time, there are legitimate concerns about the control over this technology, its potential impact on jobs and society, and ethical issues related to discrimination and the loss of human autonomy. In the talk I shall will explore and illustrate the complex tension between innovation and moral responsibility in AI research. https://www.youtube.com/watch?v=r8qK7HYq0uE https://www.youtube-nocookie.com/embed/r8qK7HYq0uE yes https://pycon.de/archive/2025/talks/reinforcement-learning-for-finance/ 2025-01-04 https://pycon.de/static/media/social/talks/VBW3EK.png Reinforcement Learning for Finance https://i.ytimg.com/vi/_n4JOMnAeCs/maxresdefault.jpg Reinforcement Learning for Finance Reinforcement Learning and related algorithms, such as Deep Q-Learning (DQL), have led to major breakthroughs in different fields. DQL, for example, is at the core of the AIs developed by DeepMind that achieved superhuman levels in such complex games as Chess, Shogi, and Go ("AlphaGo", "AlphaZero"). Reinforcement Learning can also be beneficially applied to typical problems in finance, such as algorithmic trading, dynamic hedging of options, or dynamic asset allocation. The workshop addresses the problem of limited data availability in finance and solutions to it, such as synthetic data generation through GANs. It also shows how to apply the DQL algorithm to typical financial problems. The workshop is based on my new O'Reilly book "Reinforcement Learning for Finance -- A Python-based Introduction". https://www.youtube.com/watch?v=_n4JOMnAeCs https://www.youtube-nocookie.com/embed/_n4JOMnAeCs yes https://pycon.de/archive/2025/talks/reinforcement-learning-without-a-phd-a-python-developers-journey/ 2024-12-20 https://pycon.de/static/media/social/talks/TQLGA8.png Reinforcement Learning Without a PhD: A Python Developer’s Journey https://i.ytimg.com/vi/ZIJUyf5bcww/maxresdefault.jpg Reinforcement Learning Without a PhD: A Python Developer’s Journey Reinforcement Learning (RL) has shown superhuman performance in games and is already delivering value in Big Tech. But despite its potential, RL remains largely inaccessible to most developers. Why? Because real-world RL is hard—it demands data, infrastructure, and tools that are often built for researchers, not practitioners. This talk shares the journey of applying RL to a real-world use case without having a PhD. It’s a story of figuring things out through hands-on experimentation, trial and error, and building what didn’t exist. We’ll explore what makes RL powerful, why it’s still rare in practice, and how you can get started. Along the way, you’ll learn about the key challenges of production RL, how to work around them, and how the open-source toolkit pi_optimal can help bridge the gap. Whether you're just RL-curious or ready to dive in, this talk offers practical insights and a demo to help you take your first steps. https://www.youtube.com/watch?v=ZIJUyf5bcww https://www.youtube-nocookie.com/embed/ZIJUyf5bcww yes https://pycon.de/archive/2025/talks/reinventing-streamlit/ 2024-12-16 https://pycon.de/static/media/social/talks/7CXSPN.png Reinventing Streamlit https://i.ytimg.com/vi/zE4gA0So2QY/maxresdefault.jpg Reinventing Streamlit Dreaming of creating sleek, interactive web apps with just Python? Streamlit is great for dashboards, but what if your needs go beyond that? Discover how Reflex.dev, a cutting-edge full-stack Python framework, lets you level up from dashboards to full-fledged web apps! https://www.youtube.com/watch?v=zE4gA0So2QY https://www.youtube-nocookie.com/embed/zE4gA0So2QY yes https://pycon.de/archive/2025/talks/responsible-ai-with-fmeval-an-open-source-library-to-evaluate-llms/ 2025-01-05 https://pycon.de/static/media/social/talks/KTJY9V.png Responsible AI with fmeval - an open source library to evaluate LLMs https://i.ytimg.com/vi/DHq-DcN3WTw/maxresdefault.jpg Responsible AI with fmeval - an open source library to evaluate LLMs The term "Responsible AI" has seen a threefold increase in search interest compared to 2020 across the globe. As developers, the questions like "How can we build large language model-enabled applications that are responsible and accountable to its users?" encountered in the conversation more often than before. And the discussion is further compounded by concerns surrounding uncertainty, bias, explainability, and other ethical considerations. In this session, the speaker will guide you through fmeval, an open-source library designed to evaluate Large Language Models (LLMs) across a range of tasks. The library provides notebooks that you can integrate into your daily development process, enabling you to identify, measure, and mitigate potential responsible AI issues throughout your system development lifecycle. https://www.youtube.com/watch?v=DHq-DcN3WTw https://www.youtube-nocookie.com/embed/DHq-DcN3WTw yes https://pycon.de/archive/2025/talks/rustifying-python-a-practical-guide-to-achieving-high-performance-while-maintaining-observability/ 2024-12-22 https://pycon.de/static/media/social/talks/QXSQKL.png Rustifying Python: A Practical Guide to Achieving High Performance While Maintaining Observability https://i.ytimg.com/vi/64jNiWtjCGU/maxresdefault.jpg Rustifying Python: A Practical Guide to Achieving High Performance While Maintaining Observability In this session, I’ll share our journey of migrating key parts of a Python application to Rust, resulting in over 200% performance improvement. Rather than focusing on quick Rust-to-Python integration with PyO3, this talk dives into the complexities of implementing such a migration in an enterprise environment, where reliability, scalability, and observability are crucial. You’ll learn from our mistakes, how we identified suitable areas for Rust integration, and how we extended our observability tools to cover Rust components. This session offers practical insights for improving performance and reliability in Python applications using Rust. https://www.youtube.com/watch?v=64jNiWtjCGU https://www.youtube-nocookie.com/embed/64jNiWtjCGU yes https://pycon.de/archive/2025/talks/rustzeit-asynchronous-concurrency-in-python-rust/ 2025-01-05 https://pycon.de/static/media/social/talks/FGFFEE.png 🦀 Rüstzeit: Asynchronous Concurrency in Python & Rust https://pycon.de/archive/2025/talks/safeguard-your-precious-api-endpoints-built-on-fastapi-using-oauth-2-0/ 2024-12-22 https://pycon.de/static/media/social/talks/K9ACTV.png Safeguard your precious API endpoints built on FastAPI using OAuth 2.0 https://i.ytimg.com/vi/dYwHidxANHI/maxresdefault.jpg Safeguard your precious API endpoints built on FastAPI using OAuth 2.0 Is implementing authorization on your API endpoints an afterthought? Who should have access to your API endpoints? Is it secure? This talk covers using OAuth 2.0 to secure API endpoints built on FastAPI following industry-recognized best practices. Come on a journey with me from taking your API endpoints to being functional AND secure. When you follow secure identity standards, you’ll be equipped with a deeper understanding of the critical need for authorization. https://www.youtube.com/watch?v=dYwHidxANHI https://www.youtube-nocookie.com/embed/dYwHidxANHI yes https://pycon.de/archive/2025/talks/scalable-python-and-sql-data-engineering-without-migraines/ 2025-03-24 https://pycon.de/static/media/social/talks/GVUPQN.png Scalable Python and SQL Data Engineering without Migraines https://i.ytimg.com/vi/enhjn2ShxV8/maxresdefault.jpg Scalable Python and SQL Data Engineering without Migraines This session is for data and ML engineers with a basic understanding of data engineering and Python. It shows how to easily use Python code in Snowflake Notebooks to create data pipelines. By the end, you’ll know how to build and process data pipelines with Python. https://www.youtube.com/watch?v=enhjn2ShxV8 https://www.youtube-nocookie.com/embed/enhjn2ShxV8 yes https://pycon.de/archive/2025/talks/scaling-python-an-end-to-end-ml-pipeline-for-iss-anomaly-detection-with-kubeflow/ 2024-12-20 https://pycon.de/static/media/social/talks/TRUUVL.png Scaling Python: An End-to-End ML Pipeline for ISS Anomaly Detection with Kubeflow https://i.ytimg.com/vi/MWJW22a_XnA/maxresdefault.jpg Scaling Python: An End-to-End ML Pipeline for ISS Anomaly Detection with Kubeflow Building and deploying scalable, reproducible machine learning pipelines can be challenging, especially when working with orchestration tools like Slurm or Kubernetes. In this talk, we demonstrate how to create an end-to-end ML pipeline for anomaly detection in International Space Station (ISS) telemetry data using only Python code. We show how Kubeflow Pipelines, MLFlow, and other open-source tools enable the seamless orchestration of critical steps: distributed preprocessing with Dask, hyperparameter optimization with Katib, distributed training with PyTorch Operator, experiment tracking and monitoring with MLFlow, and scalable model serving with KServe. All these steps are integrated into a holistic Kubeflow pipeline. By leveraging Kubeflow's Python SDK, we simplify the complexities of Kubernetes configurations while achieving scalable, maintainable, and reproducible pipelines. This session provides practical insights, real-world challenges, and best practices, demonstrating how Python-first workflows empower data scientists to focus on machine learning development rather than infrastructure. https://www.youtube.com/watch?v=MWJW22a_XnA https://www.youtube-nocookie.com/embed/MWJW22a_XnA yes https://pycon.de/archive/2025/talks/scraping-lego-for-fun-a-hacky-dive-into-dynamic-data-extraction/ 2024-12-24 https://pycon.de/static/media/social/talks/HSFR7A.png Scraping LEGO for Fun: A Hacky Dive into Dynamic Data Extraction https://i.ytimg.com/vi/1F_G0q3HrVk/maxresdefault.jpg Scraping LEGO for Fun: A Hacky Dive into Dynamic Data Extraction Unlock the full potential of modern web scraping by combining Python, Scrapy, and Playwright to extract data from dynamic, JavaScript-heavy sites—exemplified by LEGO product pages. This talk introduces Model Context Protocol (MCP) servers for orchestrating advanced data fetching, refining CSS selectors, and integrating Large Language Models for automated code suggestions. Learn how to scale ethically, handle concurrency, and respect site policies, while maintaining flexible, maintainable pipelines for diverse use cases from research to robotics. https://www.youtube.com/watch?v=1F_G0q3HrVk https://www.youtube-nocookie.com/embed/1F_G0q3HrVk yes https://pycon.de/archive/2025/talks/secure-human-in-the-loop-interactions-for-ai-agents/ 2025-03-10 https://pycon.de/static/media/social/talks/7RLYSQ.png Secure “Human in the Loop” Interactions for AI Agents https://i.ytimg.com/vi/vAO7fx2UAWY/maxresdefault.jpg Secure “Human in the Loop” Interactions for AI Agents Explore the power of Human-in-the-Loop (HITL) for GenAI agents! Learn how to build AI systems that augment your abilities, not replace your judgment, especially when high-stakes actions are involved. This session will focus on practical implementation using Python and Langchain to stay in control. https://www.youtube.com/watch?v=vAO7fx2UAWY https://www.youtube-nocookie.com/embed/vAO7fx2UAWY yes https://pycon.de/archive/2025/talks/securing-generative-ai-essential-threat-modeling-techniques/ 2025-01-05 https://pycon.de/static/media/social/talks/UGTB7A.png Securing Generative AI: Essential Threat Modeling Techniques https://pycon.de/archive/2025/talks/securing-rag-pipelines-with-fine-grained-authorization/ 2024-12-20 https://pycon.de/static/media/social/talks/GRWYQB.png Securing RAG Pipelines with Fine Grained Authorization https://i.ytimg.com/vi/iBg-t99bVi4/maxresdefault.jpg Securing RAG Pipelines with Fine Grained Authorization Using LLMs and AI in your Enterprise? Make sure you build Fine Grained Authorization to ensure your LLMs access only the data they are authorized to. This talk will show how you can build Relationship Based Access Control (ReBAC) for fine-grained authorization for your RAG pipelines. The talk also includes a demo using Pinecone, Langchain, OpenAI, and SpiceDB. https://www.youtube.com/watch?v=iBg-t99bVi4 https://www.youtube-nocookie.com/embed/iBg-t99bVi4 yes https://pycon.de/archive/2025/talks/serverless-orchestration-exploring-the-future-of-workflow-automation/ 2024-12-06 https://pycon.de/static/media/social/talks/AGY8CT.png Serverless Orchestration: Exploring the Future of Workflow Automation https://i.ytimg.com/vi/KgyZrqzKSZg/maxresdefault.jpg Serverless Orchestration: Exploring the Future of Workflow Automation Orchestration is a typical challenge in the data engineering world. Scheduling your data transformation jobs via CRON-jobs is cumbersome and error-prone. Furthermore, with an increasing number of jobs to manage it gets in-oversee able. Tools like Apache Airflow, Dagster, Luigi, and Prefect are known for addressing these challenges but often require additional resources or investment. With the advent of serverless orchestration tools, many of these disadvantages are mitigated, offering a more streamlined and cost-effective solution. This session provides a comprehensive overview of combining serverless architecture with orchestration. We will start by defining the core concepts of orchestration and serverless technologies and discuss the benefits of integrating them. The talk will then analyze solutions available in the cloud vendor space. Attendees will leave with a well-rounded understanding of the tools and strategies available in serverless orchestration. https://www.youtube.com/watch?v=KgyZrqzKSZg https://www.youtube-nocookie.com/embed/KgyZrqzKSZg yes https://pycon.de/archive/2025/talks/size-matters-inspecting-docker-images-for-efficiency-and-security/ 2025-01-05 https://pycon.de/static/media/social/talks/GJ9MVT.png Size matters: Inspecting Docker images for Efficiency and Security https://i.ytimg.com/vi/BCZRwKY6Zrs/maxresdefault.jpg Size matters: Inspecting Docker images for Efficiency and Security Inspecting Docker images is crucial for building secure and efficient containers. In this session, we will analyze the structure of a Python-based Docker image using various tools, focusing on best practices for minimizing image size and reducing layers with multi-stage builds. We’ll also address common security pitfalls, including proper handling of build and runtime secrets. While this talk offers valuable insights for anyone working with Docker, it is especially beneficial for Python developers seeking to master clean and secure containerization techniques. https://www.youtube.com/watch?v=BCZRwKY6Zrs https://www.youtube-nocookie.com/embed/BCZRwKY6Zrs yes https://pycon.de/archive/2025/talks/streaming-at-30-000-feet-a-real-time-journey-from-apis-to-stream-processing/ 2024-11-27 https://pycon.de/static/media/social/talks/CTUEJX.png Streaming at 30,000 Feet: A Real-Time Journey from APIs to Stream Processing https://pycon.de/archive/2025/talks/streamlining-python-deployment-with-pixi-a-perspective-from-production/ 2024-12-19 https://pycon.de/static/media/social/talks/BLKYGU.png Streamlining Python deployment with Pixi: A Perspective from production https://i.ytimg.com/vi/ScxPMrIMahY/maxresdefault.jpg Streamlining Python deployment with Pixi: A Perspective from production In our quest to improve Python deployments, we explored Pixi, a tool designed to enhance dependency management within the Conda ecosystem. This talk recounts our experience integrating Pixi into a setup used in production. We leveraged Pixi to create lockfiles, ensuring consistent builds, and to automate deployments via CI/CD pipelines. This integration led to greater reliability and efficiency, minimizing deployment errors and allowing us to concentrate more on development. Join us as we share how Pixi transformed our deployment process and offer insights into optimizing your own workflows. https://www.youtube.com/watch?v=ScxPMrIMahY https://www.youtube-nocookie.com/embed/ScxPMrIMahY yes https://pycon.de/archive/2025/talks/streamlining-the-cosmos-pythonic-workflow-management-for-astronomical-analysis/ 2024-12-18 https://pycon.de/static/media/social/talks/NBFH7G.png Streamlining the Cosmos: Pythonic Workflow Management for Astronomical Analysis https://i.ytimg.com/vi/-oT9sGgGBLI/maxresdefault.jpg Streamlining the Cosmos: Pythonic Workflow Management for Astronomical Analysis Astronomical surveys are growing rapidly in complexity and scale, necessitating accurate, efficient, and reproducible reduction and analysis pipelines. In this talk we explore Pythonic workflow managers to streamline processing large datasets on distributed computing environments. Modern astronomy generates vast datasets across the electromagnetic spectrum. NASA's flagship James Webb Space Telescope (JWST) provides unprecedented observations that enable deep studies of distant galaxies, cosmic structures, and other astrophysical phenomena. However, these datasets are complex and require intricate calibration and analysis pipelines to transform raw data into meaningful scientific insights. We will discuss the development and deployment of Pythonic tools, including snakemake and pixi, to construct modular, parallelized workflows for data reduction and analysis. Attendees will learn how these tools automate complex processing steps, optimize performance in distributed computing environments, and ensure reproducibility. Using real-world examples, we will illustrate how these workflows simplify the journey from raw data to actionable scientific insights. https://www.youtube.com/watch?v=-oT9sGgGBLI https://www.youtube-nocookie.com/embed/-oT9sGgGBLI yes https://pycon.de/archive/2025/talks/supercharge-your-testing-with-inline-snapshot/ 2025-01-02 https://pycon.de/static/media/social/talks/CRNJWQ.png Supercharge Your Testing with inline-snapshot https://i.ytimg.com/vi/G2GMVfMrswg/maxresdefault.jpg Supercharge Your Testing with inline-snapshot Snapshot tests are invaluable when you are working with large, complex, or frequently changing expected values in your tests. Introducing inline-snapshot, a Python library designed for snapshot testing that integrates seamlessly with pytest, allowing you to embed snapshot values directly within your source code. This approach not only simplifies test management but also boosts productivity by improving the maintenance of the tests. It is particularly useful for integration testing and can be used to write your own abstractions to test complex Apis. https://www.youtube.com/watch?v=G2GMVfMrswg https://www.youtube-nocookie.com/embed/G2GMVfMrswg yes https://pycon.de/archive/2025/talks/supplyseer-computational-supply-chain-with-python/ 2024-12-19 https://pycon.de/static/media/social/talks/N9CAUM.png supplyseer: Computational Supply Chain with Python https://i.ytimg.com/vi/KteUTU00Y2A/maxresdefault.jpg supplyseer: Computational Supply Chain with Python This talk introduces supplyseer, an open-source Python library that brings advanced analytics to Supply Chain and Logistics. By combining time series embedding techniques, stochastic process modeling, and geopolitical risk analysis, supplyseer helps organizations make data-driven decisions in an increasingly complex global supply chain landscape. The library implements novel approaches like Takens embedding for demand forecasting, Hawkes processes for modeling supply chain events, and Bayesian methods for inventory optimization. Through practical examples and real-world use cases, we'll explore how these mathematical concepts translate into actionable insights for supply chain practitioners. https://www.youtube.com/watch?v=KteUTU00Y2A https://www.youtube-nocookie.com/embed/KteUTU00Y2A yes https://pycon.de/archive/2025/talks/switching-from-data-scientist-to-manager/ 2025-04-24 https://pycon.de/static/media/social/talks/KDGZ8K.png Switching from Data Scientist to Manager https://pycon.de/archive/2025/talks/taking-control-of-llm-outputs-an-introductory-journey-into-logits/ 2024-12-20 https://pycon.de/static/media/social/talks/VDG9YG.png Taking Control of LLM Outputs: An Introductory Journey into Logits https://i.ytimg.com/vi/EiMPQsI2__Y/maxresdefault.jpg Taking Control of LLM Outputs: An Introductory Journey into Logits This talk explores logits - the raw confidence scores that language models generate before selecting each token. Understanding and manipulating these scores gives you practical control over how models generate text. In this introductory session, we'll explore the token-by-token generation process, examining how tokenizers work and why vocabulary matters. You'll learn about the relationship between logits, probabilities, and tokens. Then we will cover constrained decoding approaches and talk about structured generation. https://www.youtube.com/watch?v=EiMPQsI2__Y https://www.youtube-nocookie.com/embed/EiMPQsI2__Y yes https://pycon.de/archive/2025/talks/the-aesthetics-of-ai-from-cyberpunk-to-fascism/ 2024-12-22 https://pycon.de/static/media/social/talks/933YXH.png The aesthetics of AI: from cyberpunk to fascism https://i.ytimg.com/vi/P4Yj9oQ_AaE/maxresdefault.jpg The aesthetics of AI: from cyberpunk to fascism Let’s explore the visual grammars, references and cultural norms at play in the field of AI; from Kismet to Spot®, from Clippy to Claude. As a sector we can be hyper-focused on technical process and function, to the extent that it blinkers our understanding of the cultural and political impacts of our work. Aesthetics infuse every aspect of technology. Aesthetic interpretations are manifold and mutable, constructed in-congress with the observer and not fully defined by the original designer. AI technologies add additional layers of subtext: character, consciousness, agency, intent. Despite this murkiness, or perhaps because of it, this talk makes an passionate argument for engaging with historical aesthetic movements, for building our shared professional knowledge of fads and fashions⎯not just from the past 40 years of internet culture⎯but also the past 140 years of ideology, technology, and thought. https://www.youtube.com/watch?v=P4Yj9oQ_AaE https://www.youtube-nocookie.com/embed/P4Yj9oQ_AaE yes https://pycon.de/archive/2025/talks/the-earth-is-no-longer-flat-introducing-support-for-spherical-geometries-in-spherely-and-geopandas/ 2025-01-05 https://pycon.de/static/media/social/talks/AWPYGE.png The earth is no longer flat - introducing support for spherical geometries in Spherely and GeoPandas https://i.ytimg.com/vi/OcApL2JPa9s/maxresdefault.jpg The earth is no longer flat - introducing support for spherical geometries in Spherely and GeoPandas The geometries in GeoPandas, using the Shapely library, are assumed to be in projected coordinates on a flat plane. While this approximation is often just fine, for global data this runs into its limitations. This presentation introduces spherely, a Python library for working with vector geometries on the sphere, and its integration into GeoPandas. https://www.youtube.com/watch?v=OcApL2JPa9s https://www.youtube-nocookie.com/embed/OcApL2JPa9s yes https://pycon.de/archive/2025/talks/the-forecast-whisperer-secrets-of-model-tuning-revealed/ 2025-01-03 https://pycon.de/static/media/social/talks/YLKDJK.png The Forecast Whisperer: Secrets of Model Tuning Revealed https://i.ytimg.com/vi/r32xrBzEkIw/maxresdefault.jpg The Forecast Whisperer: Secrets of Model Tuning Revealed Forecasting can often feel like interpreting vague signals—unclear yet full of potential. In this talk, we’ll cover advanced techniques for tuning forecasting models in professional settings, moving beyond the basics to explore methods that enhance both accuracy and interpretability. You’ll learn: How to set clear business goals for ML model tuning and align technical work with business needs, including balancing forecast granularity and accuracy and selecting statistically correct metric. Practical data preparation methods, including business-driven data cleaning and detecting data problems with statistical and buiness driven approaches. Advanced feature selection techniques such as recursive feature elimination and SHAP values, alongside hyperparameter tuning strategies including Bayesian optimization and ensemble methods. How generative AI can support model tuning by automating feature generation, hyperparameter search, and enhancing model explainability through SHAP and LIME techniques. Real-world case studies, including how Blue Yonder’s data science team optimized demand forecasting models for retail and supply chain applications. We'll also discuss common mistakes like overfitting and data leakage, best practices for reliable validation, and the importance of domain knowledge in successful forecasting. Whether you're a seasoned data scientist or exploring time series forecasting, you'll gain advanced insights and techniques you can apply immediately. https://www.youtube.com/watch?v=r32xrBzEkIw https://www.youtube-nocookie.com/embed/r32xrBzEkIw yes https://pycon.de/archive/2025/talks/the-foundation-model-revolution-for-tabular-data/ 2024-12-20 https://pycon.de/static/media/social/talks/XRHEYZ.png The Foundation Model Revolution for Tabular Data https://i.ytimg.com/vi/9IkwXGe2Gaw/maxresdefault.jpg The Foundation Model Revolution for Tabular Data What if we could make the same revolutionary leap for tables that ChatGPT made for text? While foundation models have transformed how we work with text and images, tabular / structured data (spreadsheets and databases) - the backbone of economic and scientific analysis - has been left behind. TabPFN changes this. It's a foundation model that achieves in 2.8 seconds what traditional methods need 4 hours of hyperparameter tuning for - while delivering better results. On datasets up to 10,000 samples, it outperforms every existing Python library, from XGBoost to CatBoost to Autogluon. Beyond raw performance, TabPFN brings foundation model capabilities to tables: native handling of messy data without preprocessing, built-in uncertainty estimation, synthetic data generation, and transfer learning - all in a few lines of Python code. Whether you're building risk models, accelerating scientific research, or optimizing business decisions, TabPFN represents the next major transformation in how we analyze data. Join us to explore and learn how to leverage these new capabilities in your work. https://www.youtube.com/watch?v=9IkwXGe2Gaw https://www.youtube-nocookie.com/embed/9IkwXGe2Gaw yes https://pycon.de/archive/2025/talks/the-future-of-ai-building-the-most-impactful-technology-together/ 2025-02-24 https://pycon.de/static/media/social/talks/Z9ZTAH.png The Future of AI: Building the Most Impactful Technology Together https://i.ytimg.com/vi/evn-_i9MSsw/maxresdefault.jpg The Future of AI: Building the Most Impactful Technology Together In this talk, Leandro will examine the significant benefits of combining open source principles with artificial intelligence. He will walk through the need for openness in language models to build trust, maintain control, mitigate biases, and achieve true alignment and show how open models are rapidly gaining momentum in the AI landscape, challenging proprietary systems through community-driven innovation. Finally, he will then talk about emerging trends and what the community needs to build for the next generation of models. https://www.youtube.com/watch?v=evn-_i9MSsw https://www.youtube-nocookie.com/embed/evn-_i9MSsw yes https://pycon.de/archive/2025/talks/the-future-of-ai-training-is-federated/ 2024-12-22 https://pycon.de/static/media/social/talks/9Y9DM8.png The future of AI training is federated https://i.ytimg.com/vi/rzQNmNX2u_E/maxresdefault.jpg The future of AI training is federated Since it’s introduction in 2016, Federated Learning (FL) has become a key paradigm to AI models in scenarios when training data cannot leave its source. This applies in many industrial settings where centralizing data is challenging due to a combination of reasons, including but not limited to privacy, legal, and logistics. The main focus of this tutorial is to introduce an alternative approach to training AI models that is straightforward and accessible. We’ll walk you through the basics of an FL system, how to iterate on your workflow and code in a research setting, and finally deploy your code to a production environment. You will learn all of these approaches using a real-world application based on open-sourced datasets, and the open-source federated AI framework, [Flower](https://github.com/adap/flower), which is written in Python and designed for Python users. Throughout the tutorial, you’ll have access to hands-on open-sourced code examples to follow along. https://www.youtube.com/watch?v=rzQNmNX2u_E https://www.youtube-nocookie.com/embed/rzQNmNX2u_E yes https://pycon.de/archive/2025/talks/the-mighty-dot-customize-attribute-access-with-descriptors/ 2024-12-18 https://pycon.de/static/media/social/talks/WJPEQH.png The Mighty Dot - Customize Attribute Access with Descriptors https://i.ytimg.com/vi/UjDTAGHPcMI/maxresdefault.jpg The Mighty Dot - Customize Attribute Access with Descriptors Whenever you use a dot after an object in Python you access an attribute. While this seems a very simple operation, behind the scenes many things can happen. This tutorial looks into this mechanism that is regulated by descriptors. You will learn how a descriptor works and what kind of problems it can help to solve. Python properties are based on descriptors and solve one type of problems. Descriptors are more general, allow more use cases, and are more re-usable. Descriptors are an advanced topic. But once mastered, they provide a powerful tool to hide potentially complex behavior behind a simple dot. https://www.youtube.com/watch?v=UjDTAGHPcMI https://www.youtube-nocookie.com/embed/UjDTAGHPcMI yes https://pycon.de/archive/2025/talks/they-are-not-unit-tests-a-survey-of-unit-testing-anti-patterns/ 2024-12-21 https://pycon.de/static/media/social/talks/VFE78U.png They are not unit tests: a survey of unit-testing anti-patterns https://i.ytimg.com/vi/9MWXHbF9cPA/maxresdefault.jpg They are not unit tests: a survey of unit-testing anti-patterns The entire industry approves of unit testing but almost no one can fully agree on how to do it correctly, or even on what unit tests are. This results in unit tests often being associated with slower development cycle and an overall less enjoyable workflow. I'll show you how testing turns into hell in real enterprises with the most common anti-patterns and then I'll show you that most of them are avoidable with modern tooling like mutation testing, snapshot testing, dirty-equals, and many more. We'll discuss how to make tests speed up your development and make refactoring easy. https://www.youtube.com/watch?v=9MWXHbF9cPA https://www.youtube-nocookie.com/embed/9MWXHbF9cPA yes https://pycon.de/archive/2025/talks/topological-data-analysis-how-to-quantify-holes-in-your-data-and-why/ 2024-12-22 https://pycon.de/static/media/social/talks/HQWAYP.png Topological data analysis: How to quantify "holes" in your data and why? https://i.ytimg.com/vi/hfKdAC03bNU/maxresdefault.jpg Topological data analysis: How to quantify "holes" in your data and why? Do you need to compare sets of points in a plane? Identify a potential cyclic event in high-dimensional time series data? Find the second or the third highest peak of a noisily sampled function? Topological data analysis (TDA) is not a universal hammer, but it might just be the 16 mm wrench for your 16 mm hex head bolt. There is no shortage of Python libraries implementing TDA methods for various settings, but navigating the options can be challanging without prior familiarity with the topic. In my talk I will demonstrate the utility of the tool with several simple examples, list various libraries used by the TDA community, and dive a bit deeper into the methods to explain what the libraries implement and how to interpret and work with the outputs. https://www.youtube.com/watch?v=hfKdAC03bNU https://www.youtube-nocookie.com/embed/hfKdAC03bNU yes https://pycon.de/archive/2025/talks/towards-intelligent-monitoring-detecting-degraded-flame-torch-nozzles/ 2024-12-21 https://pycon.de/static/media/social/talks/89BX8V.png Towards Intelligent Monitoring: Detecting Degraded Flame Torch Nozzles https://i.ytimg.com/vi/boqR1M7SbiQ/maxresdefault.jpg Towards Intelligent Monitoring: Detecting Degraded Flame Torch Nozzles Flame cutting is a method where metals are efficiently cut using precise control of the oxygen jet and consistent mixing of fuel gas. The condition of the nozzle is changing over time: deposits formed during the cutting process can degrade the flame quality, reducing the precision of the cut. Traditionally, nozzles suspected of wear are sent back for manual inspection, where experts evaluated the flame visually and audibly to determine whether repair or replacement is needed. This project leverages machine learning to optimize this process by analyzing acoustic emission data. https://www.youtube.com/watch?v=boqR1M7SbiQ https://www.youtube-nocookie.com/embed/boqR1M7SbiQ yes https://pycon.de/archive/2025/talks/transformers-for-game-log-data/ 2025-01-01 https://pycon.de/static/media/social/talks/9NFHAS.png Transformers for Game Log Data https://i.ytimg.com/vi/lnMchJ22zYE/maxresdefault.jpg Transformers for Game Log Data The Transformer architecture, originally designed for machine translation, has revolutionized deep learning with applications in natural language processing, computer vision, and time series forecasting. Recently, its capabilities have extended to sequence-to-sequence tasks involving log data, such as telemetric event data from computer games. This talk demonstrates how to apply a Transformer-based model to game log data, showcasing its potential for sequence prediction and representation learning. Attendees will gain insights into implementing a simple Transformer in Python, optimizing it through hyperparameter tuning, architectural adjustments, and defining an appropriate vocabulary for game logs. Real-world applications, including clustering and user level predictions, will be explored using a dataset of over 175 million events from an MMORPG. The talk will conclude with a discussion of the model's performance, computational requirements, and future opportunities for this approach. https://www.youtube.com/watch?v=lnMchJ22zYE https://www.youtube-nocookie.com/embed/lnMchJ22zYE yes https://pycon.de/archive/2025/talks/unforgettable-that-s-what-you-are-evaluating-machine-unlearning-and-forgetting/ 2025-01-05 https://pycon.de/static/media/social/talks/SZFRRA.png Unforgettable, that's what you are: Evaluating Machine Unlearning and Forgetting https://i.ytimg.com/vi/01xCBFV5_RY/maxresdefault.jpg Unforgettable, that's what you are: Evaluating Machine Unlearning and Forgetting Can deep learning/AI models forget? In this talk, you'll explore the realm of machine unlearning, where researchers and practitioners aim to remove memorized examples from machine learning models. This is relevant for training increasingly overparameterized models and growing GDPR/Privacy concerns with large scale model development and use. https://www.youtube.com/watch?v=01xCBFV5_RY https://www.youtube-nocookie.com/embed/01xCBFV5_RY yes https://pycon.de/archive/2025/talks/unlocking-the-predictive-power-of-relational-data-with-automated-feature-engineering/ 2025-01-05 https://pycon.de/static/media/social/talks/C3RVM3.png Unlocking the Predictive Power of Relational Data with Automated Feature Engineering https://i.ytimg.com/vi/YYgHAcLzClA/maxresdefault.jpg Unlocking the Predictive Power of Relational Data with Automated Feature Engineering Relational data can be a goldmine for classical Machine Learning applications — yet extracting useful features from multiple tables, time windows, and primary-foreign key relationships is notoriously difficult. In this code tutorial, we’ll use the H&M Fashion dataset to demonstrate how getML FastProp automates feature engineering for both classification (churn prediction) and regression (sales prediction) with minimal manual effort, outperforming both Relational Deep Learning and a skilled human data scientist according to the RelBench leaderboard. This code tutorial is perfect for data scientists looking to leverage their relational and time-series data data effectively for any kind of predictive analytics applications. https://www.youtube.com/watch?v=YYgHAcLzClA https://www.youtube-nocookie.com/embed/YYgHAcLzClA yes https://pycon.de/archive/2025/talks/using-causal-thinking-to-make-media-mix-modeling/ 2024-11-28 https://pycon.de/static/media/social/talks/MNTFRG.png Using Causal thinking to make Media Mix Modeling https://i.ytimg.com/vi/JDw0RGnV2kg/maxresdefault.jpg Using Causal thinking to make Media Mix Modeling In today's data-driven landscape, understanding causal relationships is essential for effective marketing strategies. This talk will explore the link between Bayesian causal thinking and media mix modeling, utilizing Directed Acyclic Graphs (DAGs), Structural Causal Models (SCMs), and the Data Generation Process (DGP). We will examine how DAGs represent causal assumptions, how SCMs define relationships in media mix models, and how to implement these models within a Bayesian framework. By using media mix models as causal inference tools, we can estimate counterfactuals and causal effects, offering insights into the effectiveness of media investments. https://www.youtube.com/watch?v=JDw0RGnV2kg https://www.youtube-nocookie.com/embed/JDw0RGnV2kg yes https://pycon.de/archive/2025/talks/using-python-to-enter-the-world-of-microcontrollers/ 2025-01-05 https://pycon.de/static/media/social/talks/DSHASE.png Using Python to enter the world of Microcontrollers https://i.ytimg.com/vi/rmcWbJgUCfg/maxresdefault.jpg Using Python to enter the world of Microcontrollers So you've happily used the Raspberry Pi for your homelab projects, of course with Python based solutions as we all do. You've been down the rabbit hole with everything about temperature and humidity measurements, energy and solar tracking, video recording and time-lapse photography, object detection and security surveillance. You don't just buy these things of the shelve. You want to deeply understand what it takes to create such a thing, and you've been quite happy with your results so far, learned a lot. But for many simple applications ... the power draw! Yes, it's just 5 Watts you say for using a Raspberry Pi. Not a big deal in terms of cost. But you'll always need a power adapter and a free socket. You've heard of these guys using microcontrollers that run on batteries or even solar, for days, weeks, even months. That's exciting, but there's also a catch. These people write code in C-like languages, they build firmware to make their projects run. And it's all bare metal! That seems very different. That'll be a steep learning curve to take ... Or is it? Well, there's MicroPython to the rescue. Let me take you with me on a journey to make a simple microcontroller based application to read a Power Meter and send the readings over WiFi for more in depth processing somewhere else. https://www.youtube.com/watch?v=rmcWbJgUCfg https://www.youtube-nocookie.com/embed/rmcWbJgUCfg yes https://pycon.de/archive/2025/talks/vector-streaming-the-memory-efficient-indexing-for-vector-databases/ 2024-12-22 https://pycon.de/static/media/social/talks/SFDRTR.png Vector Streaming: The Memory Efficient Indexing for Vector Databases https://i.ytimg.com/vi/KRhdQ4kzsZ4/maxresdefault.jpg Vector Streaming: The Memory Efficient Indexing for Vector Databases Vector databases are everywhere, powering LLMs. But indexing embeddings, especially multivector embeddings like ColPali and Colbert, at a bulk is memory intensive. Vector streaming solves this problem by parallelizing the tasks of parsing, chunking, and embedding generation and indexing it continuously chunk by chunk instead of bulk. This not only increase the speed but also makes the whole task more optimized and memory efficient. The library gives many vector database supports, like Pinecone, Weavaite, and Elastic. https://www.youtube.com/watch?v=KRhdQ4kzsZ4 https://www.youtube-nocookie.com/embed/KRhdQ4kzsZ4 yes https://pycon.de/archive/2025/talks/what-do-a-tree-and-the-human-brain-have-in-common-a-not-so-serious-introduction-to-digital-pathology/ 2024-12-20 https://pycon.de/static/media/social/talks/MJD7TG.png What do a tree and the human brain have in common-a not so serious introduction to digital pathology https://i.ytimg.com/vi/OjVfonKKJ-A/maxresdefault.jpg What do a tree and the human brain have in common-a not so serious introduction to digital pathology While trees and human brains don't share that many properties regarding their domain, the analysis of the height of a tree and cancer in human brains does. This talk provides a not-so-serious introduction to the domain of computer vision for pathological use cases. Besides a general introduction to (digital) pathology and the technical similarities between satellite images (GeoTIFs) and pathological images (Whole-Slide Images), we will take a look at computer vision for medical tasks using Python. Whether you have never done image processing in Python, are an expert (ready to share some tricks with me), or are just curious to see pictures of a human brain, this talk is for you. Warning: this talk contains quite abstract pink-ish pictures of human tissue (and trees^^). If you are unsure this is something you are comfortable with (have a friend), do a quick search for "HE-stained whole-slide image". https://www.youtube.com/watch?v=OjVfonKKJ-A https://www.youtube-nocookie.com/embed/OjVfonKKJ-A yes https://pycon.de/archive/2025/talks/what-s-inside-the-box-building-a-deep-learning-framework-from-scratch/ 2024-12-19 https://pycon.de/static/media/social/talks/LBKU3T.png What's inside the box? Building a deep learning framework from scratch. https://i.ytimg.com/vi/LEZNNehGsCA/maxresdefault.jpg What's inside the box? Building a deep learning framework from scratch. Explore the inner workings of deep learning frameworks like TensorFlow and PyTorch by building your own in this workshop. We will start with the fundamental automatic differentiation mechanics and proceed to implementing more complex components like layers, modules and optimizers. This workshop is mainly designed for experienced data scientists, who want to expand their intuition about lower level framework internals. https://www.youtube.com/watch?v=LEZNNehGsCA https://www.youtube-nocookie.com/embed/LEZNNehGsCA yes https://pycon.de/archive/2025/talks/what-we-talk-about-when-we-talk-about-ai-skills/ 2025-01-05 https://pycon.de/static/media/social/talks/98FQDY.png What we talk about when we talk about AI skills. https://i.ytimg.com/vi/AsjEHVBpPVc/maxresdefault.jpg What we talk about when we talk about AI skills. Defining what constitutes AI skills has always been ambiguous. As AI adoption accelerates across industries and the European AI Act mandates companies to ensure AI literacy among their staff, organizations face growing even more challenges in defining and developing AI competencies. In this talk, we'll present a comprehensive framework developed by the appliedAI Institute's experts that categorizes AI skills across technical, regulatory, strategic, and innovation domains. We'll also share initial data on current AI skills levels and upskilling needs and provide practical strategies for organizations to assess, develop, and acquire the AI capabilities required for their specific needs. https://www.youtube.com/watch?v=AsjEHVBpPVc https://www.youtube-nocookie.com/embed/AsjEHVBpPVc yes https://pycon.de/archive/2025/talks/where-have-all-the-post-offices-gone-discovering-neighborhood-facilities-with-python-and-osm/ 2024-12-20 https://pycon.de/static/media/social/talks/ADSXCA.png Where have all the post offices gone? Discovering neighborhood facilities with Python and OSM https://i.ytimg.com/vi/8nCCnBvuvLE/maxresdefault.jpg Where have all the post offices gone? Discovering neighborhood facilities with Python and OSM When it comes to open geographic data, OpenStreetMap is an awesome resource. Getting started and figuring out how to make the most out of the data available can be challenging. Using a personal example: frustration at the apparent lack of post offices in my neighborhood, we'll walk through examples of how to parse, filter, process, and visualize geospatial data with Python. At the end of this talk, you will know how to process geographic data from OpenStreetMap using Python and find out some surprising info that I learned while answering the question: Where have all the post offices gone? https://www.youtube.com/watch?v=8nCCnBvuvLE https://www.youtube-nocookie.com/embed/8nCCnBvuvLE yes https://pycon.de/archive/2025/talks/why-e-on-loves-python/ 2024-12-13 https://pycon.de/static/media/social/talks/JM3G8S.png Why E.ON Loves Python https://i.ytimg.com/vi/js7L8gDIVWw/maxresdefault.jpg Why E.ON Loves Python Join me as I share my 20-year journey with Python and its pivotal role at E.ON. Discover how we transitioned fully to Python, streamlined our development framework, and embraced MLOps principles. Learn about some of our AI projects, including image analysis and real-time inference, and our steps towards open-sourcing code to foster innovation in the energy sector. Explore why Python is our go-to language for data science and collaboration. https://www.youtube.com/watch?v=js7L8gDIVWw https://www.youtube-nocookie.com/embed/js7L8gDIVWw yes https://pycon.de/archive/2025/talks/why-exceptions-are-just-sophisticated-gotos-and-how-to-move-beyond/ 2024-12-12 https://pycon.de/static/media/social/talks/S8MUBF.png Why Exceptions Are Just Sophisticated Gotos - and How to Move Beyond https://i.ytimg.com/vi/qbnn_o2nMsI/maxresdefault.jpg Why Exceptions Are Just Sophisticated Gotos - and How to Move Beyond "Why Exceptions Are Just Sophisticated Gotos - and How to Move Beyond" explores a common programming tool with a fresh perspective. While exceptions are a key feature in Python and other languages, they share surprising similarities with the notorious goto statement. This talk examines those parallels, the problems exceptions can create, and practical alternatives for better code. Attendees will gain a clear understanding of modern programming concepts and the evolution of programming. https://www.youtube.com/watch?v=qbnn_o2nMsI https://www.youtube-nocookie.com/embed/qbnn_o2nMsI yes https://pycon.de/archive/2025/talks/writing-reliable-software-while-depending-on-hazardous-apis/ 2024-12-22 https://pycon.de/static/media/social/talks/7PDARV.png Writing reliable software while depending on hazardous APIs https://i.ytimg.com/vi/4VuAD1Z47kY/maxresdefault.jpg Writing reliable software while depending on hazardous APIs As we develop business critical software, we often need to rely on external APIs to get the job done. And all services are not born equal: although the ideal world would provide well operated APIs with over-met service levels, the real world is usually way worse than that. Timeouts, HTTP errors, cascading failures, unclear or changing contracts, approximate protocol implementations ... And even the oh-so-human bad faith while trying to pinpoint the root cause... Most of us have written hacks to handle commonly seen failures, from the quick and dirty implementation to well thought resilience patterns implementation, but this is usually hard to do correctly, and rarely a business priority to invest the correct amount of time and money on the topic. We'll present the options, both including direct dependencies (not framework dependant, although some families can emerge (async/sync ...)) and including a service/proxy based approach. https://www.youtube.com/watch?v=4VuAD1Z47kY https://www.youtube-nocookie.com/embed/4VuAD1Z47kY yes https://pycon.de/archive/2025/talks/you-dont-think-about-your-streamlit-app-optimization-until-you-try-to-deploy-it-to-the-cloud/ 2025-01-03 https://pycon.de/static/media/social/talks/3VYSMS.png You don’t think about your Streamlit app optimization until you try to deploy it to the cloud https://i.ytimg.com/vi/OcGxDWNXPHU/maxresdefault.jpg You don’t think about your Streamlit app optimization until you try to deploy it to the cloud Building Streamlit apps is easy for Data Scientists - but when it’s time to deploy them to the cloud, challenges like slow model loading, scalability, and security can become major hurdles. This talk bridges two perspectives: the Data Scientist who builds the app and the MLOps engineer who deploys it. We'll dive into optimizing model loading from Hugging Face Hub, implementing features like autoscaling and authentication, and securing your app against potential threats. By the end of this talk, you’ll be ready to design Streamlit apps that are functional and deployment-ready for the cloud. https://www.youtube.com/watch?v=OcGxDWNXPHU https://www.youtube-nocookie.com/embed/OcGxDWNXPHU yes https://pycon.de/archive/2025/talks/zero-code-change-acceleration-familiar-interfaces-and-high-performance/ 2024-12-22 https://pycon.de/static/media/social/talks/PNQB7C.png Zero Code Change Acceleration: familiar interfaces and high performance https://i.ytimg.com/vi/fzAcoz3jyFo/maxresdefault.jpg Zero Code Change Acceleration: familiar interfaces and high performance The PyData ecosystem is home to some of the best and most popular tools for doing data-science. Every data-scientist alive today has used pandas and scikit-learn and even Large Language Models know how to use them! For many years there have also been alternative implementations with similar interfaces and libraries with completely new approaches that focus on achieving the ultimate in performance and hardware acceleration. This talk will look at the recent efforts to give users the best of both worlds: a familiar and widely used interface as well as high performance. https://www.youtube.com/watch?v=fzAcoz3jyFo https://www.youtube-nocookie.com/embed/fzAcoz3jyFo yes https://pycon.de/archive/2026/talks/5-years-of-nicegui-what-we-learned-about-designing-pythonic-uis/ 2026-08-04 https://pycon.de/static/media/social/talks/TZYGTL.png 5 Years of NiceGUI: What We Learned About Designing Pythonic UIs https://i.ytimg.com/vi/hfBnxtozy8A/maxresdefault.jpg 5 Years of NiceGUI: What We Learned About Designing Pythonic UIs NiceGUI has grown from a small experiment into a widely used framework for building modern web-based user interfaces entirely in Python. After five years of development, thousands of users, and countless design iterations, we have gathered a rich set of insights into what makes a UI framework feel truly “Pythonic” while still leveraging the power of the web platform. This talk presents the key lessons learned while evolving NiceGUI, with a focus on how Python’s own language features can meaningfully improve the developer experience. We explore how context managers, method chaining, decorators, async/await, type hints, dataclasses, and even well-chosen default arguments contribute to a clean, expressive, and maintainable UI API. Attendees will walk away with a deeper understanding of how to design Python-first interfaces—whether for web apps, dashboards, or internal tools—without needing to write JavaScript, CSS, or frontend boilerplate. https://www.youtube.com/watch?v=hfBnxtozy8A https://www.youtube-nocookie.com/embed/hfBnxtozy8A 2026-08-04 yes https://pycon.de/archive/2026/talks/7-anti-lessons-from-building-a-pydanticai-agent-mistakes-we-made-so-you-don-t-have-to/ 2026-08-04 https://pycon.de/static/media/social/talks/RUSUYF.png 7 Anti-Lessons from Building a PydanticAI Agent: Mistakes We Made So You Don't Have To https://i.ytimg.com/vi/MTyO3W7MYTs/maxresdefault.jpg 7 Anti-Lessons from Building a PydanticAI Agent: Mistakes We Made So You Don't Have To Life sciences compliance isn't forgiving. When your software helps companies navigate FDA regulations, ISO 13485, and EU MDR, "move fast and break things" isn't an option. Audit trails matter. Documentation is mandatory. Getting it wrong means regulatory findings, delayed product launches, or worse — patient safety risks. During the development of our AI Assistant we made every mistake in the most unforgiving environment possible. After more than a year building with PydanticAI, pydantic-evals, and Claude — nearly 3,000 commits and 20+ contributors — here are 7 anti-lessons so you don't have to repeat them: 1. **"We need a multi-agent system"** — We built one. Then deleted it. 2. **"Agents need sophisticated planning"** — A todo list beat our workflow engine. 3. **"Give the agent lots of specific tools"** — Two high-level tools replaced dozens. 4. **"Encode workflows in code"** — Markdown files the agent reads at runtime won. 5. **"It works when I test it"** — Simple tests ≠ real user journeys. Realistic evals or you're blind. 6. **"Automate everything"** — Human stays in the driver's seat, not the trunk. 7. **"Apply what made you successful before"** — Your engineering instincts might hurt you here. Real code, real git commits, real mistakes from a domain where mistakes are expensive. **Come for the mistakes. Leave with shortcuts.** https://www.youtube.com/watch?v=MTyO3W7MYTs https://www.youtube-nocookie.com/embed/MTyO3W7MYTs 2026-08-04 yes https://pycon.de/archive/2026/talks/a-minimalist-introduction-to-ansible/ 2026-08-04 https://pycon.de/static/media/social/talks/9ZKYRD.png A minimalist introduction to Ansible https://i.ytimg.com/vi/WRkWBz-JdI8/maxresdefault.jpg A minimalist introduction to Ansible [Ansible](https://docs.ansible.com/) is a popular [infrastructure as code](https://en.wikipedia.org/wiki/Infrastructure_as_code) tool for server configuration and software deployment. This tutorial will cover things that I wish the first day that I started using Ansible to manage the projects at my work. https://www.youtube.com/watch?v=WRkWBz-JdI8 https://www.youtube-nocookie.com/embed/WRkWBz-JdI8 2026-08-04 yes https://pycon.de/archive/2026/talks/a-view-of-sovereignty-from-the-cloud/ 2026-08-04 https://pycon.de/static/media/social/talks/LSJ3CN.png A View of Sovereignty from The Cloud https://i.ytimg.com/vi/GbYC0E21bFs/maxresdefault.jpg A View of Sovereignty from The Cloud While The Cloud is just someone elses computer, those computers come together from many places and many, many someone elses. The constituent parts to connect, power, house, and ultimately operate those computers are from many more places and someones still! We explore what these infrastructure pieces of The Cloud are explicitly; and how the many definitions of digital sovereignty can be viewed from the viewpoint high up in The Cloud. https://www.youtube.com/watch?v=GbYC0E21bFs https://www.youtube-nocookie.com/embed/GbYC0E21bFs 2026-08-04 yes https://pycon.de/archive/2026/talks/accelerate-fastapi-development-with-openapi-generator/ 2026-08-04 https://pycon.de/static/media/social/talks/N8QVT8.png Accelerate FastAPI Development with OpenAPI Generator https://i.ytimg.com/vi/XsV4C5w03iY/maxresdefault.jpg Accelerate FastAPI Development with OpenAPI Generator Develop FastAPI applications faster with the contract-first approach using the OpenAPI Generator, no GenAI required. **To attend this workshop, please install the openapi generator.** For details, please visit the README.md of https://gitlab.com/Eeffee/pycon26 Machine learning models are often deployed as APIs, but the "agreement" between the consumer and the service is often fragile. How does the consuming app know if a parameter is optional or required? When the code diverges from the documentation, integration breaks. In this tutorial you will learn to define an API contract using OpenAPI specification. We will use the OpenAPI Generator to automatically generate API endpoints and strictly typed Pydantic data models. Following this approach for all applications supports standardization, consistency, and maintainability across all projects. The session will cover three key areas: **Design**: We will define an OpenAPI specification as our single source of truth for the API and end consumer. **Generate**: We will use the OpenAPI Generator to create a FastAPI skeleton and show possibilities for customization to fit specific project needs. **Implement**: We will connect our generated app to a ML model where we will create Mystic Creatures for Real Life Problems https://www.youtube.com/watch?v=XsV4C5w03iY https://www.youtube-nocookie.com/embed/XsV4C5w03iY 2026-08-04 yes https://pycon.de/archive/2026/talks/accuracy-is-overrated-ship-stable-forecasts-without-lying-to-yourself/ 2026-08-04 https://pycon.de/static/media/social/talks/B8KVNJ.png Accuracy Is Overrated: Ship Stable Forecasts (Without Lying to Yourself) https://i.ytimg.com/vi/XlbwXkjTPdk/maxresdefault.jpg Accuracy Is Overrated: Ship Stable Forecasts (Without Lying to Yourself) Forecasting talks love a clean ending: “and then we improved WMAPE by 3.7%.” Nice. Now put that model into production without suffering from instability. You retrain your model on a few new weeks of data and suddenly the one-year forecast jumps 15–20%. Planning teams redo decisions, trust erodes, and your “accurate” model becomes unusable. This talk is about forecast stability: how much forecasts change when you add new data and rerun the same pipeline. We run a simple experiment: train a model, forecast one year ahead, add recent data, retrain, and measure forecast-to-forecast change. We repeat this across common forecasting approaches including ETS/ARIMA, Prophet, XGBoost with lag features, AutoGluon ensembles, neural/global models, and TimeGPT-style APIs. You will see that high accuracy does not guarantee usable forecasts, and that some models are systematically more volatile than others. We then cover practical ways to stabilise forecasts without freezing them, focusing on reconciliation and ensembling (including origin ensembling). This talk is for forecasting practitioners who want models users actually trust, not just good metrics. https://www.youtube.com/watch?v=XlbwXkjTPdk https://www.youtube-nocookie.com/embed/XlbwXkjTPdk 2026-08-04 yes https://pycon.de/archive/2026/talks/agent-based-hyperparameter-optimization-for-gradient-boosted-trees/ 2026-08-04 https://pycon.de/static/media/social/talks/BAXEXY.png Agent-Based Hyperparameter Optimization for Gradient Boosted Trees https://i.ytimg.com/vi/O6Zp5J56FCI/maxresdefault.jpg Agent-Based Hyperparameter Optimization for Gradient Boosted Trees ### Teaching an LLM to Tune GBDT — and Beyond Hyperparameter optimization for gradient boosted tree models is a repetitive yet cognitively demanding task. Practitioners must combine statistical intuition with detailed, library-specific knowledge—often buried across hundreds of pages of documentation for tools such as XGBoost, LightGBM, or CatBoost. As models and configurations grow in complexity, traditional approaches like grid search, random search, or even Bayesian optimization struggle to incorporate semantic understanding of model behavior. Using LGBM as a concrete case study, I demonstrate how MCP and skills-powered agents, orchestrated in a structured workflow, can analyze model behavior and propose targeted hyperparameter adjustments grounded in both theory and library-specific constraints. https://www.youtube.com/watch?v=O6Zp5J56FCI https://www.youtube-nocookie.com/embed/O6Zp5J56FCI 2026-08-04 yes https://pycon.de/archive/2026/talks/ai-evals-done-right-from-vibes-to-confident-decisions/ 2026-08-04 https://pycon.de/static/media/social/talks/MQJVFU.png AI Evals Done Right: From Vibes to Confident Decisions https://i.ytimg.com/vi/RFfInBj-lUI/maxresdefault.jpg AI Evals Done Right: From Vibes to Confident Decisions Testing traditional software is "simple"... same input, same output. LLMs? Not so much. Same prompt, different result every time. So how do you actually know if your AI product is good? Most teams struggle with this. Generic metrics like "Helpfulness: 4.2" sound scientific but don't drive real decisions. And when a new model releases, it's weeks of debates instead of data. This talk introduces Error Analysis: a methodology to discover the concrete failure modes of your AI product and turn them into measurable evals. You'll learn how to build a failure taxonomy that enables real prioritization. Which issues are critical? Which are frequent? What should developers fix next, and how do you measure success? The payoff: A real quality number for stakeholders. Concrete improvement tasks for developers. And when a new model drops, a ship-or-skip decision within 24 hours based on actual data. Expect a meme-powered walkthrough, real-world examples from production, and a clear path to implement this yourself starting with just 20 traces. https://www.youtube.com/watch?v=RFfInBj-lUI https://www.youtube-nocookie.com/embed/RFfInBj-lUI 2026-08-04 yes https://pycon.de/archive/2026/talks/ai-is-changing-the-game-building-modular-ai-ready-platforms-on-top-of-legacy-systems/ 2026-08-04 https://pycon.de/static/media/social/talks/HRFYVS.png AI Is Changing the Game: Building Modular, AI-Ready Platforms on Top of Legacy Systems https://i.ytimg.com/vi/CwOcGW64FN8/maxresdefault.jpg AI Is Changing the Game: Building Modular, AI-Ready Platforms on Top of Legacy Systems AI is fundamentally changing how quickly business and domain teams can create new logic, validations, and insights. In regulated environments, this new speed collides head-on with legacy systems, monolithic architectures and IT landscapes that were never designed for continuous AI-driven change. This talk presents an open, Python-based platform architecture that turns AI-driven pressure into an architectural advantage. Instead of embedding AI into existing monoliths, the platform introduces a central control layer that orchestrates independent, stateless apps—ranging from classical algorithms to AI agents—without binding them to specific infrastructure or legacy constraints. The control layer, implemented using Python and optionally Django, provides workflow orchestration, security, tenant management, and self-service registration of new components. This allows domain teams to deploy AI agents—such as anomaly detection for regulatory reporting—within days, while IT retains governance, auditability, and operational stability. The talk argues that AI will amplify architectural weaknesses—and shows why modular orchestration layers will become essential for AI-ready systems far beyond finance. https://www.youtube.com/watch?v=CwOcGW64FN8 https://www.youtube-nocookie.com/embed/CwOcGW64FN8 2026-08-04 yes https://pycon.de/archive/2026/talks/architecture-under-constraints-designing-systems-that-still-evolve/ 2026-08-04 https://pycon.de/static/media/social/talks/ZLRFR9.png Architecture Under Constraints: Designing Systems That Still Evolve https://i.ytimg.com/vi/rFd8ghVirv8/maxresdefault.jpg Architecture Under Constraints: Designing Systems That Still Evolve Most systems are built under constraints: legacy code, regulation, organizational boundaries, and long-term accountability. This talk explores how Staff+ engineers and tech leads can make sound architectural decisions when “perfect” isn’t an option. Focusing on platforms and tooling, it presents practical ways to identify real constraints, preserve flexibility, avoid over-engineering, and communicate trade-offs that hold up over time - technically and organizationally. https://www.youtube.com/watch?v=rFd8ghVirv8 https://www.youtube-nocookie.com/embed/rFd8ghVirv8 2026-08-04 yes https://pycon.de/archive/2026/talks/are-we-free-threaded-ready-looking-at-where-free-threaded-python-fails/ 2026-08-04 https://pycon.de/static/media/social/talks/FP7YN7.png Are we free-threaded ready? Looking at where free-threaded Python fails https://i.ytimg.com/vi/Fj7KlGEcS8Q/maxresdefault.jpg Are we free-threaded ready? Looking at where free-threaded Python fails Free-threaded Python aims to significantly improve performance, allowing multiple native threads to execute Python bytecode concurrently. In this talk, we will explore the current state of Python's free-threading initiative and assess its practical readiness for widespread adoption. https://www.youtube.com/watch?v=Fj7KlGEcS8Q https://www.youtube-nocookie.com/embed/Fj7KlGEcS8Q 2026-08-04 yes https://pycon.de/archive/2026/talks/array-oriented-programming-in-python-libraries-techniques-and-trade-offs/ 2026-08-04 https://pycon.de/static/media/social/talks/AGYLTV.png Array-Oriented Programming in Python: Libraries, Techniques, and Trade-offs https://i.ytimg.com/vi/zgIj2oCFF1M/maxresdefault.jpg Array-Oriented Programming in Python: Libraries, Techniques, and Trade-offs Python has become the dominant language for scientific computing and data science, largely due to powerful array libraries that enable high-performance numerical computation. This tutorial introduces array-oriented programming as a paradigm and surveys the modern Python array ecosystem. We'll explore when and how to use different array libraries: NumPy for general-purpose array operations, JAX for automatic differentiation, just-in-time compilation of array-oriented code, and GPU acceleration, Numba for just-in-time compilation of imperative code, and Awkward Array for nested and irregular data structures. Through live demos, we'll show how to think in arrays, discuss the limitations of array-oriented programming, and demonstrate how JIT compilation addresses these challenges. Whether you're analyzing data, building machine learning models, or doing scientific simulations, understanding the strengths and trade-offs of each library will help you choose the right tool for your problem. https://www.youtube.com/watch?v=zgIj2oCFF1M https://www.youtube-nocookie.com/embed/zgIj2oCFF1M 2026-08-04 yes https://pycon.de/archive/2026/talks/asyncio-vs-threads-who-survives-in-the-no-gil-era/ 2026-08-04 https://pycon.de/static/media/social/talks/UUHYUS.png AsyncIO vs Threads: who survives in the No-GIL Era? https://i.ytimg.com/vi/IN8Uvtz-eDo/maxresdefault.jpg AsyncIO vs Threads: who survives in the No-GIL Era? AsyncIO vs threads isn't about "which is faster" - it's about scheduling, memory, and the kind of load you run. We'll unpack what threads and asyncio do under the hood (OS scheduler vs event loop + epoll), run practical benchmarks, and show why many "async" libraries still rely on thread pools (aiofiles, Motor, Django bridges). Then we'll repeat the same tests on Python 3.14's free-threaded (no-GIL) build and discuss when an interpreter upgrade can beat an async rewrite. https://www.youtube.com/watch?v=IN8Uvtz-eDo https://www.youtube-nocookie.com/embed/IN8Uvtz-eDo 2026-08-04 yes https://pycon.de/archive/2026/talks/autism-and-the-predictive-brain-theory-in-tech/ 2026-08-04 https://pycon.de/static/media/social/talks/BRCNB7.png (Autism and) The Predictive Brain Theory (in Tech) https://i.ytimg.com/vi/lEYBE9Q3-90/maxresdefault.jpg (Autism and) The Predictive Brain Theory (in Tech) New studies showed how the brain is not a passive receiver of stimuli but an active predictor of stimuli. People with autism have more difficulties when the predicted and received stimuli doe not match. How do we create a tech workforce where autistic individuals can work more comfortable due to predictability? https://www.youtube.com/watch?v=lEYBE9Q3-90 https://www.youtube-nocookie.com/embed/lEYBE9Q3-90 2026-08-04 yes https://pycon.de/archive/2026/talks/before-you-ship-your-agent-an-agent-builders-primer-on-jailbreaking-attacks/ 2026-08-04 https://pycon.de/static/media/social/talks/X3KQMQ.png Before You Ship Your Agent: An Agent Builder’s Primer on Jailbreaking Attacks https://i.ytimg.com/vi/RDqJ2ZFBxgA/maxresdefault.jpg Before You Ship Your Agent: An Agent Builder’s Primer on Jailbreaking Attacks Before you ship an AI agent to production, you need to understand how it can be broken. Jailbreaking and prompt injection attacks are not edge cases—they are an inevitable consequence of deploying real-world, action-taking AI systems. This talk is a practical primer on the most common ways agents fail under adversarial pressure. We’ll break down how jailbreaking and prompt injection attacks actually work, including techniques such as excessive agency, prompt leakage, and weaknesses in vector search and embeddings. We’ll examine why popular AI guardrails consistently fail in practice, and offer little more than a false sense of protection. We’ll also address a common misconception: the absence of major AI security incidents does not mean systems are safe. Instead, it reflects limited deployment, constrained agency, and cautious rollout. As organizations adopt browser agents, autonomous tools, and systems that can take real-world actions, these vulnerabilities quickly become critical attack surfaces. This talk focuses on what organizations should do instead: applying proven security principles—least privilege, isolation, monitoring, and abuse modeling—adapted to the unique properties of AI systems. Attendees will leave with a clear understanding of the real risks, why they matter today, and the concrete steps to take before shipping an AI agent into production. https://www.youtube.com/watch?v=RDqJ2ZFBxgA https://www.youtube-nocookie.com/embed/RDqJ2ZFBxgA 2026-08-04 yes https://pycon.de/archive/2026/talks/beyond-kafka-and-s3-python-data-pipelines-with-http-native-bytestreams/ 2026-08-04 https://pycon.de/static/media/social/talks/HP7DLX.png Beyond Kafka and S3: Python Data Pipelines with HTTP-Native Bytestreams https://i.ytimg.com/vi/ViPZmGc28Ow/maxresdefault.jpg Beyond Kafka and S3: Python Data Pipelines with HTTP-Native Bytestreams Real-time bytestreams between systems in different organizations or secured environments, whether for batch dataset delivery or continuous streaming, are surprisingly hard. Traditional solutions fall short: message brokers like Kafka use discrete messages, file storage like S3 works for batch exchange but lacks streaming and coordination, while HTTP client-server approaches require one side to host and expose server endpoints, introducing security and operational overhead. This talk introduces the ZebraStream Protocol: an open, HTTP-based bytestream protocol with coordination mechanisms that let you stream data — Parquet files, compressed archives, encrypted content—directly between decoupled systems using Python's file-like interface. No message framing, no server hosting, no exposed endpoints. We'll explore the design of a bytestream protocol for data sharing and integration that crosses the file-stream boundary, enabling seamless integration with pandas and any Python library expecting file-like objects, supporting use cases from ETL pipelines to IoT data delivery, cross-org collaboration to home network automation. https://www.youtube.com/watch?v=ViPZmGc28Ow https://www.youtube-nocookie.com/embed/ViPZmGc28Ow 2026-08-04 yes https://pycon.de/archive/2026/talks/beyond-stateless-why-your-web-service-architecture-is-fighting-against-performance/ 2026-08-04 https://pycon.de/static/media/social/talks/BQYTVM.png Beyond Stateless: Why Your Web Service Architecture is Fighting Against Performance https://i.ytimg.com/vi/qzmEAwsbZzQ/maxresdefault.jpg Beyond Stateless: Why Your Web Service Architecture is Fighting Against Performance We've been told for years that stateless services are the holy grail of scalable web architectures. But what if this foundational principle is actually hurting development speed and runtime performance? Coding agents follow our example. They do what we would have done, only 10 times more. They also apply the "stateless is good" myth. This talk challenges the dominant paradigm by demonstrating how stateful, object-oriented programming can automatically scale to millions of users without the typical infrastructure complexity. I'll show how keeping objects with their state in distributed memory eliminates the need for explicit caching strategies, reduces database bottlenecks, and dramatically simplifies your code base. You'll see how a simple Python class can transparently scale across multiple servers, handling millions of concurrent users without implementing REST endpoints, message queues, or cache invalidation logic. So you can guide your agent to do scalability the right way. https://www.youtube.com/watch?v=qzmEAwsbZzQ https://www.youtube-nocookie.com/embed/qzmEAwsbZzQ 2026-08-04 yes https://pycon.de/archive/2026/talks/beyond-vibe-coding-a-practitioner-s-guide-to-spec-driven-development-in-ai-engineering/ 2026-08-04 https://pycon.de/static/media/social/talks/CVPVPK.png Beyond Vibe-Coding: A Practitioner's Guide to Spec-Driven Development in AI Engineering https://i.ytimg.com/vi/pmb9pipxFxg/maxresdefault.jpg Beyond Vibe-Coding: A Practitioner's Guide to Spec-Driven Development in AI Engineering AI-assisted coding became the default. Tools like GitHub Copilot, Cursor, and Claude can generate hundreds of lines of Python in seconds. However, the real challenge isn't how fast we generate code — it's how we ensure that generated code actually represents our intent, follows best practices, and integrates cleanly into existing systems. In this talk, I present Spec-Driven Development (SDD), a way to engineer the context in which AI writes code. Using a realistic example from my work building production-grade retrieval-augmented generation systems, I show how specifications can become a practical way to interact with AI coding tools — grounded in a concrete use case, from spec to implementation. https://www.youtube.com/watch?v=pmb9pipxFxg https://www.youtube-nocookie.com/embed/pmb9pipxFxg 2026-08-04 yes https://pycon.de/archive/2026/talks/black-hole-stars-an-astronomical-mystery-mostly-solved-with-numpyro-and-jax/ 2026-08-04 https://pycon.de/static/media/social/talks/EXXWMV.png Black Hole Stars: An Astronomical Mystery (Mostly) Solved with NumPyro and JAX https://i.ytimg.com/vi/GcaQy9K9FDc/maxresdefault.jpg Black Hole Stars: An Astronomical Mystery (Mostly) Solved with NumPyro and JAX The James Webb Space Telescope has revealed a mysterious population of "Little Red Dots": extremely distant objects that have upended our understanding of the early Universe. However, revealing the true nature of these marvels requires computationally-intensive statistical modeling of complex astronomical data. In this talk, we explore how we used JAX and NumPyro to help solve this puzzle. We will introduce these powerful Python tools, demonstrate how they accelerate complex statistical data analysis, and show how they provided evidence that Little Red Dots may in fact be "Black Hole Stars." https://www.youtube.com/watch?v=GcaQy9K9FDc https://www.youtube-nocookie.com/embed/GcaQy9K9FDc 2026-08-04 yes https://pycon.de/archive/2026/talks/build-a-web-coding-platform-with-python-run-in-webassembly/ 2026-08-04 https://pycon.de/static/media/social/talks/BFYYQG.png Build a web coding platform with Python, run in WebAssembly https://i.ytimg.com/vi/XUesgXAIzpc/maxresdefault.jpg Build a web coding platform with Python, run in WebAssembly Ever wanted to build a website that can run python, but you're worried about running user submitted code on your server? In this talk I'll show how Holoviz Panel can create an interactive coding environment where students can write functions, solve exercises, and experiment safely, all while their code runs locally via WebAssembly. https://www.youtube.com/watch?v=XUesgXAIzpc https://www.youtube-nocookie.com/embed/XUesgXAIzpc 2026-08-04 yes https://pycon.de/archive/2026/talks/building-agentic-systems-with-python-langgraph-mcp-and-a2a/ 2026-08-25 https://pycon.de/static/media/social/talks/JBFGCA.png Building Agentic Systems with Python, LangGraph, MCP, and A2A https://i.ytimg.com/vi/rJKBnHYicQA/maxresdefault.jpg Building Agentic Systems with Python, LangGraph, MCP, and A2A Building an agentic system that collects and evaluates company information in real time—without curated datasets—requires solving difficult challenges in data acquisition, quality control, and agent orchestration. This talk outlines the solution design for such a system, implemented with Python-based tooling including LangGraph, and emerging protocols such as A2A and MCP, within a multi-agent workflow. Because MCP and A2A are still new and lightly documented, we will share implementation lessons and a practical example of a hub-and-spoke architecture based on a recent real-world system. Attendees will learn architectural patterns for multi-agent systems, common pitfalls of using MCP/A2A in real-world scenarios, and strategies for maintaining data quality in agent-based workflows. https://www.youtube.com/watch?v=rJKBnHYicQA https://www.youtube-nocookie.com/embed/rJKBnHYicQA 2026-08-25 yes https://pycon.de/archive/2026/talks/building-mcp-at-the-speed-of-hype-principles-that-outlast-the-trends/ 2026-08-04 https://pycon.de/static/media/social/talks/EPASS8.png Building MCP at the Speed of Hype: Principles That Outlast the Trends https://i.ytimg.com/vi/hTkJ-Hm8_1Q/maxresdefault.jpg Building MCP at the Speed of Hype: Principles That Outlast the Trends Every week, development in AI brings us another groundbreaking release, another model version, another must-have integration. In this rapidly shifting landscape, how does one build production systems that won’t be obsolete by the time you deploy them? We'll explain how trusting in proven engineering principles from software development and machine learning, like separation of concerns and evaluation practices, became our anchor in an ever-changing landscape of AI development. We share lessons learned from building two MCP applications using FastMCP and PydanticAI. Against these challenges, we found that fundamental engineering principles provided the foundation we needed. Participants in the process of developing AI tools will leave with practical strategies for building AI-powered systems that are flexible enough to adapt, yet stable enough to trust. https://www.youtube.com/watch?v=hTkJ-Hm8_1Q https://www.youtube-nocookie.com/embed/hTkJ-Hm8_1Q 2026-08-04 yes https://pycon.de/archive/2026/talks/building-non-biased-synthetic-datasets-what-actually-works-and-what-fails/ 2026-08-04 https://pycon.de/static/media/social/talks/M33SNJ.png Building Non-Biased Synthetic Datasets: What Actually Works (and What Fails) https://i.ytimg.com/vi/z38zHsLnBVk/maxresdefault.jpg Building Non-Biased Synthetic Datasets: What Actually Works (and What Fails) Synthetic data is often presented as an easy fix for missing or sensitive datasets, but in practice, it can silently introduce bias, leakage, and misleading evaluation results. This talk presents a practical, end-to-end pipeline for creating synthetic datasets that are reproducible, task-aligned, and bias-aware. We will walk through design decisions that matter: template-based generation vs. free-form generation, entity balancing, controlling distributional skew, filtering failure cases, and validating dataset quality before training any model. The session emphasizes what actually works in real pipelines, common failure modes that look fine at first glance, and concrete best practices for Python developers to apply when building synthetic datasets for machine learning, NLP, or evaluation. https://www.youtube.com/watch?v=z38zHsLnBVk https://www.youtube-nocookie.com/embed/z38zHsLnBVk 2026-08-04 yes https://pycon.de/archive/2026/talks/building-reliable-data-pipelines-with-polars-and-dataframely/ 2026-08-04 https://pycon.de/static/media/social/talks/GPJGH3.png Building reliable data pipelines with polars and dataframely https://i.ytimg.com/vi/08tyYLgfaBg/maxresdefault.jpg Building reliable data pipelines with polars and dataframely If you have worked with real-world data before, you know that processing it can be challenging. Data often comes scattered across tables, in inconsistent encodings, with duplicated rows and is generally dirty. In this tutorial, you will learn how to process large amounts of data reliably and quickly using `polars` and `dataframely`. What we love about `polars` is that it's easy to use, fast and elegant — it allows us to build and compose complex transformations with ease. On this basis, we built `dataframely`: a library for defining and validating contents of polars data frames. With `dataframely`, we can build pipelines without ever getting confused about what's in our data frames. We document and validate our expectations and assumptions clearly, which makes our pipeline code simpler and easier to understand. "Is this join correct?", and "where did this column come from?" are questions you will not have to worry about anymore. In this tutorial, you will become familiar with `polars` basics by writing a simple pipeline: you will read data, transform it to make it ready for use, and you will learn how to do that fast. With `dataframely` schemas, you will upgrade your code from "it works" to "it's beautiful!", and along the way, `dataframely` will help you eliminate entire classes of bugs you will never have to think about again. After the tutorial, you will be all set to use these tools in your own work. https://www.youtube.com/watch?v=08tyYLgfaBg https://www.youtube-nocookie.com/embed/08tyYLgfaBg 2026-08-04 yes https://pycon.de/archive/2026/talks/building-secure-environments-for-cli-code-agents/ 2026-08-25 https://pycon.de/static/media/social/talks/3BYLZU.png Building Secure Environments for CLI Code Agents https://i.ytimg.com/vi/PEJakKxVh1c/maxresdefault.jpg Building Secure Environments for CLI Code Agents AI code agents like Claude Code are powerful but require careful isolation. Learn how to run them in secure containers with persistent credentials, API logging, and complete filesystem isolation—protecting your host system while maintaining full functionality. https://www.youtube.com/watch?v=PEJakKxVh1c https://www.youtube-nocookie.com/embed/PEJakKxVh1c 2026-08-25 yes https://pycon.de/archive/2026/talks/building-trust-in-your-data-pipelines-with-observability/ 2026-08-04 https://pycon.de/static/media/social/talks/U9KQU9.png Building Trust in Your Data Pipelines with Observability https://i.ytimg.com/vi/M0lHvt1PWiQ/maxresdefault.jpg Building Trust in Your Data Pipelines with Observability In the daily work of a data engineer, building new data pipelines often takes priority, while maintaining them and ensuring their correctness becomes an afterthought. This focus can quickly turn into a pitfall: failures go undetected, incorrect data silently propagates, and complaints from stakeholders arrive before engineers notice any issues. In practice, incorporating observability into every new data pipeline helps avoid these problems and enables teams to steadily increase system complexity while maintaining trust and peace of mind. In this talk, I introduce observability in the context of data pipelines, covering its three core pillars: metrics, alarms, and logs. We will explore concepts like the four golden signals, alarm fatigue and structured logging and how they apply to data pipelines. I will show easy to implement first steps and share real-world experiences, where improved observability helped uncover previously unknown incorrect behavior and build trust in data systems. This talk is well suited for data engineers that had little exposure to observability and want to learn about strategies how to keep sane while managing a jungle of pipelines. https://www.youtube.com/watch?v=M0lHvt1PWiQ https://www.youtube-nocookie.com/embed/M0lHvt1PWiQ 2026-08-04 yes https://pycon.de/archive/2026/talks/catch-the-llm-if-you-can-watermarking-llms/ 2026-08-04 https://pycon.de/static/media/social/talks/3JLSEF.png Catch the LLM if you Can: Watermarking LLMs https://i.ytimg.com/vi/VMS66nOlZEc/maxresdefault.jpg Catch the LLM if you Can: Watermarking LLMs With Large Language Models (LLMs), generating high-quality text and images is easy and so is misusing it. As AI-generated content becomes harder to distinguish from human generated content, developers are increasingly asking: How can we verify whether a piece of text comes from an LLM? We’ll explore Python’s simplicity and rich ecosystem of libraries to solve this problem. This talk introduces the foundations of LLM watermarking and shows how developers can implement these techniques entirely in Python. We’ll discuss two core approaches, EXP sampling method and KGW method. We will go through the implementation of the KGW method using simple, transparent code, and compare it with the EXP approach. There's no need for a large model or a GPU cluster to understand how these systems work and the core ideas can be implemented in pure Python using simple code. The code repositories, which includes both methods will be provided so that the attendees can follow along. Along the way, we’ll discuss the trade-offs and the limitations of current research. And for those wondering, “Do I have to implement all this myself?”, the talk concludes with a quick overview of MarkLLM, an existing open-source toolkit that provides a unified Python interface for experimenting with watermarking algorithms. Attendees will leave with a clear understanding of how watermarking works, when it’s useful, and how to integrate these techniques into real-world Python projects. https://www.youtube.com/watch?v=VMS66nOlZEc https://www.youtube-nocookie.com/embed/VMS66nOlZEc 2026-08-04 yes https://pycon.de/archive/2026/talks/causal-inference-through-the-lens-of-probabilistic-programming/ 2026-08-04 https://pycon.de/static/media/social/talks/Q9DU8N.png Causal Inference through the lens of probabilistic programming https://i.ytimg.com/vi/SCflGCEyWF8/maxresdefault.jpg Causal Inference through the lens of probabilistic programming Causal inference asks the hardest question in data science: "What would have happened if things were different?" While traditional methods often rely on rigid rules, statistical tests or "black box" adjustments, Probabilistic Programming Languages (PPLs) like PyMC and NumPyro offer a transparent, flexible, and powerful lens to view these problems. In this talk, we move beyond the standard "correlation is not causation" disclaimer. We will build a unified workflow that starts with robust A/B testing, moves to bias adjustment in observational data using multilevel models, and culminates with advanced Deep Causal Latent Variable Models (CEVAE). https://www.youtube.com/watch?v=SCflGCEyWF8 https://www.youtube-nocookie.com/embed/SCflGCEyWF8 2026-08-04 yes https://pycon.de/archive/2026/talks/closing-session/ 2026-08-04 https://pycon.de/static/media/social/talks/9ZEFTR.png Closing Session https://i.ytimg.com/vi/sOL_xSUoUWo/maxresdefault.jpg Closing Session Closing Session https://www.youtube.com/watch?v=sOL_xSUoUWo https://www.youtube-nocookie.com/embed/sOL_xSUoUWo 2026-08-04 yes https://pycon.de/archive/2026/talks/come-for-the-code-stay-for-the-people/ 2026-08-04 https://pycon.de/static/media/social/talks/PK8XNB.png Come for the Code, Stay for the People. https://i.ytimg.com/vi/k8UwAKP8rEE/maxresdefault.jpg Come for the Code, Stay for the People. "Come for the language, stay for the community." If you've been around Python long enough, you've heard this before. I don't know when I first heard it, but I know exactly when I understood it. This talk is a personal reflection on seventeen years within the Python community—from my first tentative steps as a volunteer to organising conferences myself. It's a story about discovering that Python was always about more than code. It's about the people, the values, and the unexpected ways a community can shape a career and a life. This isn't just my story. It's a story I've seen repeated in countless faces at registration desks, in hallway conversations, in first-time speakers finding their voice. I want to talk about what I've learned about kindness, mentorship, and the quiet power of feeling like you belong somewhere. I'll end with an open question: as the ways we connect continue to evolve, how do we preserve what matters while welcoming a new generation? If you're new to this community and wondering what all the fuss is about, this talk is especially for you. https://www.youtube.com/watch?v=k8UwAKP8rEE https://www.youtube-nocookie.com/embed/k8UwAKP8rEE 2026-08-04 yes https://pycon.de/archive/2026/talks/demystifying-agentic-ai-using-small-language-models/ 2026-08-25 https://pycon.de/static/media/social/talks/YZM8TA.png Demystifying Agentic AI Using Small Language Models https://i.ytimg.com/vi/nk5BNz8v_9E/maxresdefault.jpg Demystifying Agentic AI Using Small Language Models The AI world is buzzing with claims about “agentic intelligence” and autonomous reasoning. Behind the hype, however, a quieter shift is taking place: Small Language Models (SLMs) are proving capable of many reasoning tasks once assumed to require massive LLMs. When paired with fresh business data from modern lakehouses and accessed through tool calling, these models can power surprisingly capable agents. In this talk, we cut through the noise around “agents” and examine what actually works today. You’ll see how compact models such as Phi-2 or xLAM-2 can reason and invoke tools effectively, and how to run them on development laptops or modest clusters for fast iteration. By grounding agents in business facts stored in Iceberg tables, hallucinations are reduced, while Iceberg’s read scalability enables thousands of agents to operate in parallel on a shared source of truth. Attendees will leave with a practical understanding of data agent architectures, SLM capabilities, Iceberg integration, and a realistic path to deploying useful data agents - without a GPU farm. https://www.youtube.com/watch?v=nk5BNz8v_9E https://www.youtube-nocookie.com/embed/nk5BNz8v_9E 2026-08-25 yes https://pycon.de/archive/2026/talks/demystifying-containers-with-python-building-a-minimal-engine-from-scratch/ 2026-08-04 https://pycon.de/static/media/social/talks/BLC7FS.png Demystifying Containers with Python: Building a Minimal Engine from Scratch https://i.ytimg.com/vi/Yzo40hvli9g/maxresdefault.jpg Demystifying Containers with Python: Building a Minimal Engine from Scratch Containers are a fundamental part of the modern developer's toolkit, yet they are frequently misunderstood and described as "lightweight virtual machines." This talk demystifies containerization by building a functional, minimal engine from scratch using only the python standard library. We will step away from high-level tools like docker to explore how the linux kernel provides isolation through features like `namespaces` and `chroot`. Using a hands-on approach, we will demonstrate how to set up a sandboxed environment, isolate a filesystem, and execute processes within it. This session is designed for developers who use containers daily but haven't yet had the opportunity to look under the hood or explore the underlying operating system principles. By implementing a simplified version of these tools, you will gain a clearer, more practical understanding of the core mechanics that make containerization possible. https://www.youtube.com/watch?v=Yzo40hvli9g https://www.youtube-nocookie.com/embed/Yzo40hvli9g 2026-08-04 yes https://pycon.de/archive/2026/talks/demystifying-parallel-programming-in-python-from-cpu-to-quantum-processors-including-gpu-and-tpu/ 2026-08-04 https://pycon.de/static/media/social/talks/HBFL78.png Demystifying Parallel Programming in Python: from CPU to quantum processors, including GPU and TPU https://i.ytimg.com/vi/FsJM7TGwc-A/maxresdefault.jpg Demystifying Parallel Programming in Python: from CPU to quantum processors, including GPU and TPU This talk provides a beginner-friendly overview of Python’s parallel programming ecosystem. You’ll discover the key libraries and techniques—JIT compilation, multithreading, multiprocessing, distributed computing, HPC/grid computing, and even a first look at quantum programming—to help you write faster, more efficient code, regardless of your hardware. https://www.youtube.com/watch?v=FsJM7TGwc-A https://www.youtube-nocookie.com/embed/FsJM7TGwc-A 2026-08-04 yes https://pycon.de/archive/2026/talks/designing-and-scaling-a-python-library-in-the-open-architecture-automation-and-community/ 2026-08-04 https://pycon.de/static/media/social/talks/VBPRQR.png Designing and Scaling a Python Library in the Open: Architecture, Automation and Community https://i.ytimg.com/vi/aBeLg5Op2lw/maxresdefault.jpg Designing and Scaling a Python Library in the Open: Architecture, Automation and Community Designing a Python library that scales over time requires more than clean code. In this talk, we present ScanAPI, an open-source Python library for automated API integration testing and live documentation, as a case study in sustainable library design. We explore how architectural decisions, Python features, and automation pipelines help reduce maintenance costs while improving developer experience. We also share how open collaboration and community practices turn a Python library into a long-term, scalable project. Attendees will leave with practical patterns to apply when building or evolving Python libraries in the open. https://www.youtube.com/watch?v=aBeLg5Op2lw https://www.youtube-nocookie.com/embed/aBeLg5Op2lw 2026-08-04 yes https://pycon.de/archive/2026/talks/destructive-testing-10-practical-ways-to-expose-hidden-application-risks/ 2026-08-04 https://pycon.de/static/media/social/talks/YKQ33N.png Destructive Testing: 10 Practical Ways to Expose Hidden Application Risks https://i.ytimg.com/vi/7a_L-7PRo7Y/maxresdefault.jpg Destructive Testing: 10 Practical Ways to Expose Hidden Application Risks Modern applications rarely fail in obvious ways. Instead, they break at the edges: unexpected inputs, race conditions, misused APIs, and assumptions nobody realized they were making. This talk presents ten practical and repeatable ways to intentionally break an application, using a QA mindset with a strong Python focus. The session is designed to help QAs sharpen their investigative approach and move beyond happy-path testing, while giving developers concrete insight into where real-world failures often originate. Each “way to break an application” highlights a common risk area such as data handling, state management, timing, configuration, or integration boundaries. Attendees will learn how to think more destructively (in a productive way), design better tests, and recognize fragile design decisions earlier. The goal is not to assign blame, but to improve collaboration and software quality by understanding how systems actually fail in practice. https://www.youtube.com/watch?v=7a_L-7PRo7Y https://www.youtube-nocookie.com/embed/7a_L-7PRo7Y 2026-08-04 yes https://pycon.de/archive/2026/talks/django-q2-async-tasks-made-simple/ 2026-08-04 https://pycon.de/static/media/social/talks/BHJERV.png Django-Q2: Async Tasks Made Simple https://i.ytimg.com/vi/xGadHTaIZ8I/maxresdefault.jpg Django-Q2: Async Tasks Made Simple Managing asynchronous task queues in Django with tools like Celery can be overkill for many projects. Django-Q2 is a lightweight alternative that integrates natively with the Django admin. In this talk, you will learn how to streamline your background tasks and cron jobs, featuring a practical demo to get you started immediately. https://www.youtube.com/watch?v=xGadHTaIZ8I https://www.youtube-nocookie.com/embed/xGadHTaIZ8I 2026-08-04 yes https://pycon.de/archive/2026/talks/do-you-know-how-well-your-model-is-doing-evaluate-your-llms/ 2026-08-04 https://pycon.de/static/media/social/talks/QX8DDJ.png Do you know how well your model is doing? Evaluate your LLMs https://i.ytimg.com/vi/lpTeJ0WpWyE/maxresdefault.jpg Do you know how well your model is doing? Evaluate your LLMs Large Language Models (LLMs) are becoming central to modern applications, yet effectively evaluating their performance remains a significant challenge. How do you objectively compare different models, benchmark the impact of fine-tuning, or ensure your LLM responses adhere to safety guidelines (guard-railing)? This hands-on workshop addresses these critical questions. https://www.youtube.com/watch?v=lpTeJ0WpWyE https://www.youtube-nocookie.com/embed/lpTeJ0WpWyE 2026-08-04 yes https://pycon.de/archive/2026/talks/dont-call-your-llm-too-often-how-to-build-your-dialog-graph-with-confidence-and-sleep-at-night/ 2026-08-04 https://pycon.de/static/media/social/talks/EWZMJK.png Don’t call your LLM too often! How to build your dialog graph with confidence and sleep at night. https://i.ytimg.com/vi/AHePPIVx31s/maxresdefault.jpg Don’t call your LLM too often! How to build your dialog graph with confidence and sleep at night. Keywords: **Explainable AI, enhanced RAG, GraphRAG, LLMOps, dialog system evaluation.** Designing reliable dialog flows for LLM-based systems remains challenging once conversations require branching, correction, or multi-step reasoning. Dialog graphs often evolve organically and accumulate structural issues: endless correction loops, dead subpaths, redundant validation steps, overly generic catch-all branches, or linear sequences that should be collapsed. Such phenomena raise operational costs, significantly increase TTFT and make the system answer less predictable and explainable. Many solutions try to introduce an all-fit generalized RAG retrieval solution. Contrary to this, we present our empirical learnings on how to enhance system speed, lower overall costs and offer a better dialog graph explainability through enhanced LLM call tracing and iterative enhancements for common dialog paths. We also show that more elaborated knowledge retrieval strategies like GraphRAG may drastically enhance overall response quality and shorten the dialog graph. We evaluate several approaches and give recommendations on how to leverage more complex document indexing phases for inference time benefits. Overall, the session argues that scalable conversational systems require not only better prompts, but explicit graph structures paired with rigorous tracing and data-driven optimization. https://www.youtube.com/watch?v=AHePPIVx31s https://www.youtube-nocookie.com/embed/AHePPIVx31s 2026-08-04 yes https://pycon.de/archive/2026/talks/dont-let-imposter-syndrome-win-u-can-do-big-things-from-a-small-place-a-7-year-african-ai-journey/ 2025-12-18 https://pycon.de/static/media/social/talks/V7LQGR.png Don’t Let Imposter Syndrome Win: U Can Do Big Things from a Small Place, A 7-Year African AI Journey https://pycon.de/archive/2026/talks/dynamic-knowledge-graphs/ 2026-08-04 https://pycon.de/static/media/social/talks/TST9LF.png Dynamic Knowledge Graphs https://i.ytimg.com/vi/tDy8UzEO2cg/maxresdefault.jpg Dynamic Knowledge Graphs Traditional RAG systems struggle to understand holistic connections in distributed, constantly changing knowledge sources that characterize real-world organizations. While document-based approaches using vector embeddings provide basic retrieval, they fail to capture relationships and answer complex questions about interconnected information. Graph-based RAG offers a solution, but existing implementations like Microsoft's GraphRAG explicitly avoid dynamic operations due to complexity, requiring costly rebuilds when knowledge changes. This talk introduces a production-ready dynamic knowledge graph system that supports real-time insertion, querying, and deletion of information. Through practical implementation details you will learn to build maintainable knowledge graphs that evolve with data, handle ambiguous entities and preserve information lineage. https://www.youtube.com/watch?v=tDy8UzEO2cg https://www.youtube-nocookie.com/embed/tDy8UzEO2cg 2026-08-04 yes https://pycon.de/archive/2026/talks/embedding-data-science-in-iot-devices-with-micropython-and-emlearn/ 2026-08-04 https://pycon.de/static/media/social/talks/3U3BZH.png Embedding Data Science in IoT devices with MicroPython and emlearn https://i.ytimg.com/vi/lwTfY3Eh1dw/maxresdefault.jpg Embedding Data Science in IoT devices with MicroPython and emlearn Python is the standard solution for many machine learning and data science applications, from large cloud systems, to workstations, and even on larger embedded or robotics systems. But as we move down into more constrained environments regular (C)Python starts to be a less good fit. The MicroPython project provides a Python implementation that is tailored for such environments, and this makes it possible scale down to microcontrollers with just a few megabytes of RAM (or less!). As a bonus, MicroPython with WebAssembly also makes lightweight browser applications possible. In this talk, we will discuss how to combine Internet of Things (IoT) hardware, MicroPython and browser to build stand-alone smart sensor systems and laboratory gear for physical data science. https://www.youtube.com/watch?v=lwTfY3Eh1dw https://www.youtube-nocookie.com/embed/lwTfY3Eh1dw 2026-08-04 yes https://pycon.de/archive/2026/talks/empowering-data-scientists-with-zero-platform-friction-deploying-streamlit-friends-in-3-minutes/ 2026-08-04 https://pycon.de/static/media/social/talks/9MUDUY.png Empowering Data Scientists with Zero Platform Friction: Deploying Streamlit & Friends in 3 Minutes https://i.ytimg.com/vi/qzTcTtxO-AE/maxresdefault.jpg Empowering Data Scientists with Zero Platform Friction: Deploying Streamlit & Friends in 3 Minutes A data scientist builds a Streamlit or Dash prototype, the business wants to validate it, and the hard parts begin: getting access to live data, making the app available company-wide, and ensuring every user only sees what they are allowed to see. Following "best practices" turn a simple demo into weeks of platform work, leaving data scientists frustrated and blocking them from shipping apps to end users. In this talk we will **live-demo** Merck's self-service app service we have developed and hardened over multiple years. It lets **teams deploy Streamlit (and friends) in 3 minutes** while meeting best practices like SSO, CI/CD, and governed data access control. The platform has become essential for Merck to ship data apps at scale: in 2025 it powered **750+ active apps** reaching **8,000+ unique end users**. **Under the hood, we show:** how a use-case based access model enables scoped resource permissions so apps can safely access data on-behalf of the user. We also show starter templates that generate a deployable Git repo with example pages (e.g. Snowflake access or internal LLM chatbot). Finally, we cover the guardrails needed to operate this safely. **What you will learn:** a cost-effective reference architecture based on AWS that you can adapt to your hyperscaler or platform, practical patterns for balancing the trade-off between central control and decentral freedom, and how templates and CI/CD help teams iterate quickly without compromising security or reliability. https://www.youtube.com/watch?v=qzTcTtxO-AE https://www.youtube-nocookie.com/embed/qzTcTtxO-AE 2026-08-04 yes https://pycon.de/archive/2026/talks/escape-the-hype-teaching-llm-concepts-through-an-interactive-ai-factory-game/ 2026-08-04 https://pycon.de/static/media/social/talks/MS7AWK.png Escape the Hype: Teaching LLM Concepts Through an Interactive AI Factory Game https://i.ytimg.com/vi/XabrJzyMhx8/maxresdefault.jpg Escape the Hype: Teaching LLM Concepts Through an Interactive AI Factory Game Everyone talks about LLMs, RAG, and AI agents - but who truly understands them? Marketing promises magic while documentation assumes expertise. Recent research from Gartner reveals the consequences: only 8% of HR leaders believe their managers possess adequate AI competency, while companies that restructure work around AI achieve revenue goals twice as often as those who merely train employees. The problem isn't lack of information; it's the lack of genuine understanding through experience. We took a different approach. Instead of slides or tutorials, we built "AI Factory" - a non-profit educational platform in the form of escape room game where players learn by doing. Craft prompts under budget pressure. Watch guardrails fail in real-time. Break their own RAG pipeline. Each mistake teaches more than any documentation ever could. In this talk, we'll share what we discovered while building and testing this game with real users: why failure-driven learning outperforms tutorials, how game mechanics create memorable "aha moments," and the surprising concepts that clicked only through play. https://www.youtube.com/watch?v=XabrJzyMhx8 https://www.youtube-nocookie.com/embed/XabrJzyMhx8 2026-08-04 yes https://pycon.de/archive/2026/talks/exploring-germany-s-urban-geography-with-census-and-openstreetmap-data/ 2026-08-04 https://pycon.de/static/media/social/talks/WQGXJ3.png Exploring Germany's Urban Geography with Census and OpenStreetMap Data https://i.ytimg.com/vi/1LEXE8H0BaE/maxresdefault.jpg Exploring Germany's Urban Geography with Census and OpenStreetMap Data When conducting studies of the urban form, an important resource many researchers turn to is the massive OpenStreetMap dataset. But, as extensive as this dataset is, it lacks one very important aspect about the cities it covers: the people who live there. In this talk, I show you how to add this missing element to your research by bringing in German Census data to create rich analysis capable of answering some of the most pressing issues facing our cities today. I exemplify this by walking you through my own research in urban geography and sustainability with a study of how equitably distributed emergency care hospitals are in cities across Germany. Throughout, we look at how Python and PostgreSQL can be used as effective tools to enable this research and keep it organized. https://www.youtube.com/watch?v=1LEXE8H0BaE https://www.youtube-nocookie.com/embed/1LEXE8H0BaE 2026-08-04 yes https://pycon.de/archive/2026/talks/fight-your-garbage-data-implementation-of-a-pythonic-data-quality-monitoring-framework-in-pyspark/ 2026-08-04 https://pycon.de/static/media/social/talks/PFXR9G.png Fight your garbage data: implementation of a pythonic data quality monitoring framework in PySpark https://i.ytimg.com/vi/7ZJJlj0i_TM/maxresdefault.jpg Fight your garbage data: implementation of a pythonic data quality monitoring framework in PySpark The timeless phrase “garbage in, garbage out” is even more important today with the growing usage of non-deterministic generative neuronal networks, which amplifies the effect of bad data quality. This presentation describes Data Quality Monitor — a tool to bring transparency into data quality and help drive real improvements. In the talk, we'll cover what defines a successful data quality monitoring solution and share findings from our initial evaluation of available open-source frameworks. Next, we'll showcase our implementation based on DQX. DQX is a lightweight, open-source framework for performing row-level data quality checks programmatically, with business rules organized in manageable YAML files. DQX, originally developed by Databricks Labs, integrates seamlessly with PySpark, making it easy and affordable to run data quality checks within our IoT data lake. Finally, we will discuss the organizational processes and structures required to effectively respond to data quality issues. https://www.youtube.com/watch?v=7ZJJlj0i_TM https://www.youtube-nocookie.com/embed/7ZJJlj0i_TM 2026-08-04 yes https://pycon.de/archive/2026/talks/foundation-models-in-forecasting-are-we-there-yet-lessons-from-the-trenches/ 2026-08-04 https://pycon.de/static/media/social/talks/KQM8JJ.png Foundation Models in Forecasting: Are We There Yet? Lessons from the Trenches https://i.ytimg.com/vi/vS8WREuJ3-M/maxresdefault.jpg Foundation Models in Forecasting: Are We There Yet? Lessons from the Trenches The rise of time-series foundation models like Chronos-2 and TimesFM has sparked a debate: can a single pre-trained model replace the specialized "local" models we have tuned for years? We moved beyond the hype to test these models in production-like environments, from high-level market trends to granular article-level demand. In this talk, we share a transparent look at our journey: the zero-shot capabilities of these models, the reality of fine-tuning with exogenous business drivers, and a comparison between generative models and state-of-the-art classical methods. We categorize what is currently possible, what remains a challenge, and provide a roadmap for teams looking to integrate foundation models into their forecasting stack without sacrificing reliability. https://www.youtube.com/watch?v=vS8WREuJ3-M https://www.youtube-nocookie.com/embed/vS8WREuJ3-M 2026-08-04 yes https://pycon.de/archive/2026/talks/free-t-h-r-e-ading-a-trading-systems-journey-beyond-the-gil/ 2026-08-04 https://pycon.de/static/media/social/talks/NDZSSB.png Free T(h)r(e)ading: A Trading Systems Journey Beyond the GIL https://i.ytimg.com/vi/p3VQih2R4rc/maxresdefault.jpg Free T(h)r(e)ading: A Trading Systems Journey Beyond the GIL Python 3.13's free-threaded mode opens new territory for Python concurrency. We embarked on an experiment: could a trading algorithm benefit from true parallelism, and what would it take to get there? This talk documents our research journey from async/await to free threading—the hypotheses we tested, the benchmarks we designed, the unexpected behaviours we discovered, and the systematic approach we took to validating whether GIL-free Python could handle real-time market data. You'll see our experimental methodology, the data we collected, surprising findings about thread scheduling and memory patterns, and what our results suggest about Python's concurrent future. https://www.youtube.com/watch?v=p3VQih2R4rc https://www.youtube-nocookie.com/embed/p3VQih2R4rc 2026-08-04 yes https://pycon.de/archive/2026/talks/from-hard-problems-to-proven-solutions-solving-decision-problems-with-gurobi/ 2026-08-04 https://pycon.de/static/media/social/talks/333HDN.png From Hard Problems to Proven Solutions: Solving Decision Problems with Gurobi https://i.ytimg.com/vi/CdEJxZ-fpVY/maxresdefault.jpg From Hard Problems to Proven Solutions: Solving Decision Problems with Gurobi Join us as we demonstrate how to formulate and solve hard decision problems with Gurobi. You’ll learn practical modeling techniques that integrate naturally with NumPy, SciPy.sparse, and pandas. We’ll show how mathematical optimization computes reliable solutions with provable guarantees — enabling robust, transparent decision-making. https://www.youtube.com/watch?v=CdEJxZ-fpVY https://www.youtube-nocookie.com/embed/CdEJxZ-fpVY 2026-08-04 yes https://pycon.de/archive/2026/talks/from-pixel-to-payouts-a-multi-agent-system-for-real-time-insurance-claims-processing/ 2026-08-04 https://pycon.de/static/media/social/talks/APWGQB.png From Pixel to Payouts: A Multi-Agent System for Real-Time Insurance Claims Processing https://i.ytimg.com/vi/LNki_OFpxf0/maxresdefault.jpg From Pixel to Payouts: A Multi-Agent System for Real-Time Insurance Claims Processing The traditional process for auto damage evaluation is relatively slow, subjective, and prone to fraud. With this presentation, the goal is to show a Multi-Agent System designed for the automation and standardization in real-time of the car damage evaluation, disrupting the initial claims workflow. The system is built around an Orchestrator Agent with the role to coordinate specialized AI agents: a Vision Agent (powered by OpenAI GPT-5.2) for damage analysis and severity classification, two Cost Estimation Agents (powered by Perplexity's sonar-pro) to provide comparative quotes (OEM vs. Aftermarket), and a Shop Finder Agent for local repair options. The system produces a report that includes a description of the damage, severity, comparative repair costs in local currency, and recommended repair shops, all embedded into a Gradio/Streamlit interface. The task of this approach is to reduce the processing time, improve transparency for customers, and provide insurers with objective data to enable faster claims resolution. https://www.youtube.com/watch?v=LNki_OFpxf0 https://www.youtube-nocookie.com/embed/LNki_OFpxf0 2026-08-04 yes https://pycon.de/archive/2026/talks/from-prompt-to-production-how-to-use-ai-code-assistants-for-python-data-systems/ 2026-08-04 https://pycon.de/static/media/social/talks/RRLTBU.png From Prompt to Production: How to use AI Code Assistants for Python Data Systems https://i.ytimg.com/vi/t6oX0fbQHAY/maxresdefault.jpg From Prompt to Production: How to use AI Code Assistants for Python Data Systems **Code-generating LLMs have matured** to the point where they can reliably scaffold **data pipelines and data agents**, when used in a **supervised, engineering-first workflow**. This tutorial demonstrates how to combine modern **AI coding assistants** with a **production-ready Python deployment platform (Tower.dev)** to build and operate **real data systems**. Participants will learn how to structure **collaborative Human/AI Assistant development loops**, where engineers provide **architecture, domain knowledge, and review**, while AI accelerates implementation. We will build a **data pipeline** and a **lightweight data agent**, iterating with an AI assistant to **generate, test, and improve code**. The session also covers critical **operational concerns** such as: - **Security** - **Scaling** - **Observability** - **Debugging** You will also see how **production feedback can be looped back into the assistant** to continuously improve generated code. This is **not about “vibe coding”** a website. It is about **disciplined, review-driven AI collaboration** that meaningfully improves productivity for **data practitioners at all levels**. https://www.youtube.com/watch?v=t6oX0fbQHAY https://www.youtube-nocookie.com/embed/t6oX0fbQHAY 2026-08-04 yes https://pycon.de/archive/2026/talks/from-research-models-to-slas-operationalizing-tsfms-with-python/ 2026-08-25 https://pycon.de/static/media/social/talks/3XDMXS.png From Research Models to SLAs: Operationalizing TSFMs with Python https://i.ytimg.com/vi/QkBtqERDlpg/maxresdefault.jpg From Research Models to SLAs: Operationalizing TSFMs with Python Time series foundation models (TSFMs) such as Chronos, Lag-Llama, TimesFM, and Siemens’ own GTT have shown strong generalization capabilities across diverse forecasting tasks. However, integrating these models into a large organization is primarily a software engineering and MLOps challenge rather than a modeling one. In this talk, we present a real-world case study based on Siemens KPI Forecast, a Python-based forecasting platform that operationalizes multiple TSFMs as reusable, production-grade services. The platform integrates both open research models and Siemens-developed models behind a unified API, supporting zero-shot inference, fine-tuning jobs, and fine-tuned inference depending on user needs and operational constraints. We focus on how Python is used to compose heterogeneous components including open and closed-source models, internal data products, APIs, and orchestration layers into a consistent time series specialist user experience. The session also covers challenges operating such services within a B2B environment, including issues related to monitoring, versioning, and governance. Attendees will gain practical insights into turning TSFMs into reliable Python services that scale across teams and use cases. https://www.youtube.com/watch?v=QkBtqERDlpg https://www.youtube-nocookie.com/embed/QkBtqERDlpg 2026-08-25 yes https://pycon.de/archive/2026/talks/from-row-wise-to-columnar-speeding-up-pyspark-udfs-with-arrow-and-polars/ 2026-08-04 https://pycon.de/static/media/social/talks/FT7V39.png From Row-Wise to Columnar: Speeding Up PySpark UDFs with Arrow and Polars https://i.ytimg.com/vi/Jho624IrxpM/maxresdefault.jpg From Row-Wise to Columnar: Speeding Up PySpark UDFs with Arrow and Polars Python UDFs often become the slowest part of PySpark pipelines because they run row-by-row and pay a high cost crossing the JVM↔Python boundary. Spark’s Arrow-backed execution changes that cost model by moving data in columnar batches, which can reduce overhead and enable efficient, vectorized processing in Python. In this session, we’ll cover practical patterns for writing Arrow-friendly UDF logic and integrating it with fast Python execution engines that operate on Arrow data. We’ll compare common approaches—scalar UDFs, Pandas UDFs, Arrow-native UDFs, and table-shaped Arrow transforms—then translate the results into a decision guide you can apply to production pipelines. Attendees will leave knowing when Arrow helps, when it doesn’t, and how to design UDF-heavy transformations that scale. https://www.youtube.com/watch?v=Jho624IrxpM https://www.youtube-nocookie.com/embed/Jho624IrxpM 2026-08-04 yes https://pycon.de/archive/2026/talks/from-scratch-to-scale-turning-llm-code-into-architecture-insights/ 2026-08-04 https://pycon.de/static/media/social/talks/BFL7MQ.png From Scratch to Scale: Turning LLM Code into Architecture Insights https://i.ytimg.com/vi/FLlHh5sIQ7w/maxresdefault.jpg From Scratch to Scale: Turning LLM Code into Architecture Insights Python has been at the center of my work in machine learning and AI for more than a decade. It is where I start from scratch, experiment with ideas, and build systems that help me understand how large language models really work. In this keynote, we will explore how Python enables this entire journey, from defining model architectures and training loops to scaling data and computation across devices. I will also reflect on how Python continues to support both the large models of today and the evolving systems of tomorrow, even as new backends take over the heavy lifting. https://www.youtube.com/watch?v=FLlHh5sIQ7w https://www.youtube-nocookie.com/embed/FLlHh5sIQ7w 2026-08-04 yes https://pycon.de/archive/2026/talks/from-struggling-to-mastery-a-practical-guide-to-data-pipeline-operations/ 2026-08-04 https://pycon.de/static/media/social/talks/PARU7X.png From Struggling to Mastery: A Practical Guide to Data Pipeline Operations https://i.ytimg.com/vi/dDHQKv8D70E/maxresdefault.jpg From Struggling to Mastery: A Practical Guide to Data Pipeline Operations How mature are your data pipeline operations? A Roadmap to Operational Excellence. Data teams often struggle to scale their pipeline operations, trapped in a cycle of manual fixes and reactive fire-fighting. But what does "good" actually look like? In this talk, we introduce a standardized 5-level maturity model for Data Operations, focusing on three critical pillars: Orchestration, Data Quality, and Data SLOs. We will deconstruct the journey from "Struggling" (manual scripts, no guarantees) to "Mastery" (automated, resilient, and measured). Attendees will leave with a concrete framework to assess their team’s current standing and a clear, step-by-step roadmap to raise the bar toward operational excellence. https://www.youtube.com/watch?v=dDHQKv8D70E https://www.youtube-nocookie.com/embed/dDHQKv8D70E 2026-08-04 yes https://pycon.de/archive/2026/talks/from-ticket-to-draft-how-munich-automates-citizen-inquiries-with-ai/ 2026-08-04 https://pycon.de/static/media/social/talks/39MHWT.png From Ticket to Draft: How Munich Automates Citizen Inquiries with AI https://i.ytimg.com/vi/9Sfxy2nmUU0/maxresdefault.jpg From Ticket to Draft: How Munich Automates Citizen Inquiries with AI The City of Munich is modernizing its communication: With the transition to the Zammad ticketing system, there is a unique opportunity to not only manage citizen inquiries but to proactively process them using Artificial Intelligence. The Zammad-AI project utilizes a two-stage process consisting of intelligent classification and RAG-based (Retrieval-Augmented Generation) response drafting to significantly reduce the workload of administrative staff. In this talk, we demonstrate how we integrated Zammad-AI via an internal Kafka message bus to process tickets in real-time. We explore the technical workflow—from thematic context analysis to the generation of valid response drafts based on a department-specific knowledge base. https://www.youtube.com/watch?v=9Sfxy2nmUU0 https://www.youtube-nocookie.com/embed/9Sfxy2nmUU0 2026-08-04 yes https://pycon.de/archive/2026/talks/getting-career-clarity-in-uncertain-times/ 2026-08-04 https://pycon.de/static/media/social/talks/DDVW3W.png Getting Career Clarity in Uncertain Times https://i.ytimg.com/vi/3e-U9L94ruo/maxresdefault.jpg Getting Career Clarity in Uncertain Times Feeling unsure about your next step in your career? The data & AI field is evolving faster than ever. New tools, new roles, and constant “next big things” can make even experienced professionals feel unsure about where they are heading, and how to make intentional career decisions in the middle of all this change. You might be doing well, feeling comfortable. Interesting work, steady progress, recognition. And still, there’s that question in the background: Where is this actually going? This interactive workshop helps you explore different future paths, understand trade-offs, and gain clarity about what kind of work and influence you want next. https://www.youtube.com/watch?v=3e-U9L94ruo https://www.youtube-nocookie.com/embed/3e-U9L94ruo 2026-08-04 yes https://pycon.de/archive/2026/talks/heat-scaling-the-python-scientific-stack-to-hpc-systems/ 2026-08-04 https://pycon.de/static/media/social/talks/K9LCNQ.png Heat: scaling the Python scientific stack to HPC systems https://i.ytimg.com/vi/DonT6K6eiOI/maxresdefault.jpg Heat: scaling the Python scientific stack to HPC systems Python’s scientific stack (NumPy/SciPy) is often confined to single-node execution. When datasets exceed local memory, researchers face a steep learning curve, typically choosing between complex manual distribution or the overhead of task-parallel frameworks. In this talk, we introduce [Heat](https://github.com/helmholtz-analytics/heat), an open-source distributed tensor framework designed to bring high-performance computing (HPC) capabilities to the scientific Python ecosystem. Built on PyTorch and mpi4py, Heat implements a data-parallel model that allows users to process massive datasets across multi-node, multi-GPU clusters (including AMD GPUs) with minimal code changes. We will discuss the design and architecture enabling "transparent distribution": - Heat’s distributed n-dimensional array for data partitioning and communication under the hood; - The synergy of PyTorch as a high-performance compute engine and MPI for efficient, low-latency communication; - Scaling efficiency, encompassing both strong and weak scaling for memory-intensive operations; - Fundamental building blocks—from linear algebra to machine learning—re-implemented for distributed memory space. Attendees will learn how to leverage the cumulative RAM of supercomputers without leaving the familiar NumPy-like interface, effectively removing the "memory wall" for large-scale scientific analytics. https://www.youtube.com/watch?v=DonT6K6eiOI https://www.youtube-nocookie.com/embed/DonT6K6eiOI 2026-08-04 yes https://pycon.de/archive/2026/talks/hierarchical-models-in-mmm-can-structure-beat-data-size/ 2026-08-04 https://pycon.de/static/media/social/talks/EL7X8C.png Hierarchical Models in MMM: Can Structure beat data size? https://i.ytimg.com/vi/9lExGn-JLFg/maxresdefault.jpg Hierarchical Models in MMM: Can Structure beat data size? In every marketing project, teams strive to find more data, a longer timeframe, and more detailed splits, just to fix noisy channel attribution. But what if structure played a bigger role than size and volume? In this talk, we try to prove this. Using a simple toolkit like Arviz and PyMC, we show you a simple hierarchical mix model, and how, by applying partial pooling, we can stabilize important KPIS like ROAS estimates across sparse channels- without the need for more data. We will go through the code, transformation, and the real-life practices that allow us to get as close to the truth, to be able to have a meaningful impact in the marketing world. The approach will be centered around marketing mix models, different transformations, and how useful it will be for the business. https://www.youtube.com/watch?v=9lExGn-JLFg https://www.youtube-nocookie.com/embed/9lExGn-JLFg 2026-08-04 yes https://pycon.de/archive/2026/talks/holistic-optimization-implementing-pipeline-as-a-trial-hpo-with-ray-and-cloud-infra/ 2026-08-04 https://pycon.de/static/media/social/talks/GPV9SM.png Holistic Optimization: Implementing "Pipeline-as-a-Trial" HPO with Ray and Cloud Infra https://i.ytimg.com/vi/J0U3h9sYAE8/maxresdefault.jpg Holistic Optimization: Implementing "Pipeline-as-a-Trial" HPO with Ray and Cloud Infra Most hyperparameter optimization (HPO) stops at the model boundary. But what happens when your system relies on a complex chain of steps, a short-horizon model, a long-horizon model, ensembles, postprocesses etc? Tuning one piece in isolation often leads to sub-optimal global results. In this talk, we explore how we used Ray to move beyond simple model tuning. We’ll dive into a "Pipeline-as-a-Trial" architecture where Ray acts as the brain, triggering independent, scalable cloud workflows ( SageMaker Pipelines or Databricks Workflows) for every hyperparameter set. We will discuss: * The architectural shift from tuning models to tuning pipelines * How to build the DAG/pipeline on Sagemaker/Databricks using declarative configs * How to use Ray to orchestrate heavyweight remote jobs without bottlenecks. Attendees will learn how to optimize entire pipelines (in a scalable manner on cloud) to minimize global metrics like WAPE, rather than just local model loss. https://www.youtube.com/watch?v=J0U3h9sYAE8 https://www.youtube-nocookie.com/embed/J0U3h9sYAE8 2026-08-04 yes https://pycon.de/archive/2026/talks/honey-i-vibe-coded-some-crypto-security-in-the-age-of-llms/ 2026-08-04 https://pycon.de/static/media/social/talks/CMDHUN.png "Honey, I vibe coded some crypto" - Security in the age of LLMS https://i.ytimg.com/vi/xZMgvEANRRE/maxresdefault.jpg "Honey, I vibe coded some crypto" - Security in the age of LLMS What only a few years ago started out as smart tab completion turned into a way of working in which a growing number of programmers don't even bother to open up an IDE anymore. Let's take a moment to contemplate the changing nature of software engineering as a profession, and to explore chances to avoid looming disaster. https://www.youtube.com/watch?v=xZMgvEANRRE https://www.youtube-nocookie.com/embed/xZMgvEANRRE 2026-08-04 yes https://pycon.de/archive/2026/talks/how-to-compare-apples-with-oranges-proper-evaluation-of-article-level-demand-forecasts/ 2026-08-04 https://pycon.de/static/media/social/talks/TB9WYZ.png How to compare apples with oranges: Proper evaluation of article-level demand forecasts https://i.ytimg.com/vi/W4rjcvSzB1A/maxresdefault.jpg How to compare apples with oranges: Proper evaluation of article-level demand forecasts How do you evaluate performance when you predict more than 10 million time series each day? While a good plot can be worth more than a thousand metrics for a single time series, with large-scale machine learning models implemented with *LightGBM* and *PyTorch* we have to resort to meaningful aggregations. We will share insights and learnings from the past 2 years of deploying and operating our article-level demand forecasting models at the pricing department of Zalando. This talk moves beyond basic metrics to showcase the pitfalls of aggregated error measures and the best practices we’ve developed to keep our stakeholders informed and our models accurate. https://www.youtube.com/watch?v=W4rjcvSzB1A https://www.youtube-nocookie.com/embed/W4rjcvSzB1A 2026-08-04 yes https://pycon.de/archive/2026/talks/how-to-create-effective-data-visualizations/ 2026-08-04 https://pycon.de/static/media/social/talks/GBKUNF.png How to create effective data visualizations https://i.ytimg.com/vi/VC_G_lqideY/maxresdefault.jpg How to create effective data visualizations What distinguishes a lousy plot from a beautiful chart that communicates insights effectively? This talk will show you the underlying principles of good data visualization, offer lots of practical tips and tricks and give an overview of the data visualization landscape in Python. After the talk, you will be able to create better charts, whether for exploring your own data or for communicating results to others. https://www.youtube.com/watch?v=VC_G_lqideY https://www.youtube-nocookie.com/embed/VC_G_lqideY 2026-08-04 yes https://pycon.de/archive/2026/talks/how-to-mix-conda-and-pip-without-causing-environmental-damage/ 2026-08-04 https://pycon.de/static/media/social/talks/7H9DF8.png How to mix conda and pip without causing “environmental” damage. https://i.ytimg.com/vi/f-KFuzRCCso/maxresdefault.jpg How to mix conda and pip without causing “environmental” damage. Ever mixed conda and pip and ended up with a broken conda environment, yet, swear it worked before? This talk explains why! Learn the difference between pip and conda, what happens when you mix them and how to combine them safely using the latest community developed tools and updates in conda. https://www.youtube.com/watch?v=f-KFuzRCCso https://www.youtube-nocookie.com/embed/f-KFuzRCCso 2026-08-04 yes https://pycon.de/archive/2026/talks/how-to-search-through-800-billion-records-in-real-time/ 2026-08-04 https://pycon.de/static/media/social/talks/ZYUJH3.png How to Search Through 800 Billion Records in Real Time https://i.ytimg.com/vi/t0ZWNh-UXDs/maxresdefault.jpg How to Search Through 800 Billion Records in Real Time Large-scale distributed systems rarely produce clean data streams. In practice, hundreds of services continuously emit overlapping updates, retries, corrections, and partial state. Turning that constant stream of noisy events into a reliable, searchable dataset in real time, while processing hundreds of billions of records per day, requires careful architectural choices. This talk shares practical lessons from building a Kafka-based ETL pipeline that transforms massive volumes of events into a coherent dataset suitable for real-time search. After a brief overview of the system architecture, we focus on several key techniques: reducing redundant processing through key deduplication and short-lived buffers, defining when messages can be safely acknowledged without risking data loss, and keeping long-running ETL services healthy under heavy Kafka workloads. The session emphasizes concrete engineering trade-offs and operational realities rather than theory. Attendees will leave with practical patterns for building more reliable and efficient streaming pipelines. https://www.youtube.com/watch?v=t0ZWNh-UXDs https://www.youtube-nocookie.com/embed/t0ZWNh-UXDs 2026-08-04 yes https://pycon.de/archive/2026/talks/how-we-built-an-inclusive-data-organization-careers-community-50-women/ 2026-08-04 https://pycon.de/static/media/social/talks/RQTJFS.png How We Built an Inclusive Data Organization: Careers, Community & 50% Women https://i.ytimg.com/vi/ThYFPq6AtMU/maxresdefault.jpg How We Built an Inclusive Data Organization: Careers, Community & 50% Women Building inclusive data teams and sustainable career paths is a challenge many organizations struggle with—especially in fast-growing, highly technical environments. Data careers are often portrayed as linear, while diversity initiatives remain abstract or ineffective in practice. This talk shares concrete, experience-based lessons from building an inclusive data organization that supports career growth, fosters an internal data science community, and achieved more than 50% women representation in data roles. Rather than focusing on theory, the session highlights practical decisions, structural changes, and leadership behaviors that made inclusion measurable and sustainable. Attendees will gain actionable insights into designing career paths that support non-linear journeys, creating internal data communities that encourage learning and collaboration, and implementing diversity practices that strengthen—rather than dilute—technical excellence. The talk is relevant for data scientists, engineers, team leads, and managers who want to build better teams and healthier data cultures. https://www.youtube.com/watch?v=ThYFPq6AtMU https://www.youtube-nocookie.com/embed/ThYFPq6AtMU 2026-08-04 yes https://pycon.de/archive/2026/talks/hype-hope-or-headache-making-sense-of-genai-llms-and-ai-agents-with-anecdotal-evidence/ 2026-08-04 https://pycon.de/static/media/social/talks/WSNBD9.png Hype, Hope, or Headache? Making Sense of GenAI, LLMs, and AI Agents with Anecdotal Evidence https://i.ytimg.com/vi/NxxXpmXa3ws/maxresdefault.jpg Hype, Hope, or Headache? Making Sense of GenAI, LLMs, and AI Agents with Anecdotal Evidence After nearly 20 years in data science, from MLPs, SVMs, and random forests to deep learning, I’ve seen many “revolutions” come and go. The current tectonic shift around GenAI and LLMs feels different from previous hype cycles. Even with some understanding how these things work, I am still blown away by the stream of stunning new capabilities. But they also introduce new kinds of risks that go far beyond technical performance. This talk offers a pragmatic, experience-driven perspective on GenAI in industrial settings, including supply chains and the emerging wave of AI agents. We’ll disentangle real opportunities from snake oil, especially where hype-driven promises meet senior management expectations. An anti-bullshit take on the possibilities ahead, with honesty, anecdotes, and (for those who know me, of course) a bit of humor. https://www.youtube.com/watch?v=NxxXpmXa3ws https://www.youtube-nocookie.com/embed/NxxXpmXa3ws 2026-08-04 yes https://pycon.de/archive/2026/talks/in-praise-of-documentation-tools-tips-techniques-for-literate-programming-in-the-ai-age/ 2026-08-04 https://pycon.de/static/media/social/talks/37AESH.png In Praise of Documentation: Tools, Tips & Techniques for Literate Programming in the AI Age https://i.ytimg.com/vi/ToTCJIWEeaw/maxresdefault.jpg In Praise of Documentation: Tools, Tips & Techniques for Literate Programming in the AI Age This talk has one simple message: *please document your code*. If you attend my talk, you'll hear me explain why I praise documentation, and why you should too. While writing documentation is generally acknowledged to be a "good thing", most engineers do not document their work. I'll offer my optionated lament on the life and death of literate programming. A lament is a poetic discourse, expressing sadness, or feeling sorry about something. I'll give some examples of the *bad things* that can happen when people don't write documentation. Then, after making you feel bad, I'll give examples of how you can *feel good*. I'll explain why writing documentation is a "good" edifying activity, which helps you to be a better person, and make a better world. I'll review types of open source documentation (Python and Unix), documentation frameworks (Diátaxis), and Python tools (Sphinx, Jupyter, Quarto) you can try out as soon as my talk is finished. Then, I'll get "cool n' futuristic" by talking about AI. I'll emphasise the importance of text to AI-assisted coding and agentic workflows for "spec-driven development" (e.g. Agent-OS with Claude Code), before tempering your excitement by giving you some old-fashioned advice on "good" writing style by George Orwell. In summary, if you come to my talk, you might experience an unusual mixture of sadness combined with hope. To conclude, I'll tell you to "please document your code". You'll laugh, go to the next talk, and forget my advice. https://www.youtube.com/watch?v=ToTCJIWEeaw https://www.youtube-nocookie.com/embed/ToTCJIWEeaw 2026-08-04 yes https://pycon.de/archive/2026/talks/increase-productivity-of-cnc-machining-of-aerospace-engine-parts-with-python/ 2026-08-04 https://pycon.de/static/media/social/talks/FJQXEQ.png Increase productivity of CNC-machining of aerospace engine parts with Python https://i.ytimg.com/vi/z1RqFPujec4/maxresdefault.jpg Increase productivity of CNC-machining of aerospace engine parts with Python Increasing unit labour costs and the imperative need to reduce energy consumption raises the necessity to enhance productivity in industrial production. Python is an excellent tool for GKN Aerospace, as the world’s leading tier one aerospace supplier, to address the needs for higher utilization and unmanned operation on the shopfloor on its site in Kongsberg, Norway. As an example, the presentation shares insight into the in-house developed “Production Execution System”, consisting of a Python backend and a REACT frontend. The application orchestrates all necessary data on cell-level, like NC-programs and additional digital services of the company’s IT environment during unmanned production. Furthermore, it supports the operator with necessary information to ensure highest quality of engine parts in a work environment of increasing digitalization and workload. https://www.youtube.com/watch?v=z1RqFPujec4 https://www.youtube-nocookie.com/embed/z1RqFPujec4 2026-08-04 yes https://pycon.de/archive/2026/talks/innovation-day-startup-lounge-no-video/ 2026-04-12 https://pycon.de/static/media/social/talks/KBNKAP.png Innovation Day: Startup Lounge [no-video] https://pycon.de/archive/2026/talks/is-digital-sovereignty-a-new-buzzword-in-ai-development/ 2026-08-04 https://pycon.de/static/media/social/talks/QMBEZX.png Is digital sovereignty a new buzzword in AI development? https://i.ytimg.com/vi/krOl3wTI78g/maxresdefault.jpg Is digital sovereignty a new buzzword in AI development? AI development usually focuses on feasibility and implementation, but a new buzzword is now being used: 'sovereignty'. While customers are excited about it, what does it mean for them and for AI developers? In this presentation, we analyse different aspects of sovereignty and explore how it can be used to build trustworthy AI solutions. We will also discuss current examples from politics and development to identify the best practices for secure data processing. https://www.youtube.com/watch?v=krOl3wTI78g https://www.youtube-nocookie.com/embed/krOl3wTI78g 2026-08-04 yes https://pycon.de/archive/2026/talks/is-my-ai-recruiting-biased-how-to-evaluate-these-systems/ 2026-08-04 https://pycon.de/static/media/social/talks/3C9P9V.png Is my AI Recruiting biased? - How to evaluate these systems https://i.ytimg.com/vi/ViSx_2hVqhY/maxresdefault.jpg Is my AI Recruiting biased? - How to evaluate these systems AI recruiting systems are increasingly used to filter, rank, and select applicants at scale. Yet their deployment raises essential questions: How reliable are these models in real hiring environments, and how do we ensure fairness and safety across diverse applicant profiles? This talk presents a structured approach to testing and validating AI-driven recruiting pipelines. It highlights the role of synthetic test data, data augmentation, and fairness metrics in uncovering systemic risks and mitigating bias. Attendees will walk through a complete evaluation workflow. The session also incorporates insights from real-world testing practices, demonstrating how rigorous validation can increase trust and transparency in recruitment AI. https://www.youtube.com/watch?v=ViSx_2hVqhY https://www.youtube-nocookie.com/embed/ViSx_2hVqhY 2026-08-04 yes https://pycon.de/archive/2026/talks/it-works-on-my-machine-why-llm-apps-fail-users-not-tests/ 2026-08-04 https://pycon.de/static/media/social/talks/3UHPZB.png It Works on My Machine: Why LLM Apps Fail Users (Not Tests) https://i.ytimg.com/vi/0Mh271tYYmQ/maxresdefault.jpg It Works on My Machine: Why LLM Apps Fail Users (Not Tests) LLM applications frequently pass tests but fail users in production. This talk examines the gap between evaluation metrics and user experience through three lenses: **Expectations** (what "working" means to users), **Functional** (system-level vs. component-level success), and **Operational** (real-world reliability). Drawing from production experience, we'll share scenarios of expectation mismatches, silent failures, and undetected drift—plus practical strategies for bridging the gap. The core message: evaluation should answer whether your system serves users, not whether it passes tests. https://www.youtube.com/watch?v=0Mh271tYYmQ https://www.youtube-nocookie.com/embed/0Mh271tYYmQ 2026-08-04 yes https://pycon.de/archive/2026/talks/kickstart-coding-at-scale-how-project-template-automation-unlocks-developer-productivity/ 2026-08-04 https://pycon.de/static/media/social/talks/AWFFUS.png Kickstart Coding at Scale: How Project Template Automation Unlocks Developer Productivity https://i.ytimg.com/vi/HATspzsbLwk/maxresdefault.jpg Kickstart Coding at Scale: How Project Template Automation Unlocks Developer Productivity As your company grows, so does your software landscape. Different CI configurations, inconsistent linting rules, varying packaging approaches: every new project reinvents the wheel. Scaffolding tools like cookiecutter help with the initial setup — but what happens six months later, when best practices have evolved, and your template has moved on? That’s where most approaches fall apart. And a centralized, “magic” pipeline is no better — it’s opaque, brittle, and leaves no room for customization. Using Copier, we built a standardized yet customizable project template — a paved road that guides developers without boxing them in. But the real game changer is what comes after: Copier’s built-in update mechanism lets us propagate template improvements to hundreds of existing projects. A GitHub bot runs monthly, opens Pull Requests with the latest changes, and a Streamlit dashboard tracks adoption across the organization. Attendees will learn how to build flexible templates, automate ongoing maintenance at scale, and manage version drift — so developers can focus on writing code instead of fighting boilerplate. https://www.youtube.com/watch?v=HATspzsbLwk https://www.youtube-nocookie.com/embed/HATspzsbLwk 2026-08-04 yes https://pycon.de/archive/2026/talks/learnings-building-devops-as-a-software-engineer/ 2026-08-04 https://pycon.de/static/media/social/talks/ZESFRG.png Learnings Building DevOps as a Software Engineer https://i.ytimg.com/vi/RZK9iBGd9rY/maxresdefault.jpg Learnings Building DevOps as a Software Engineer When I joined my current company as a software engineer, I encountered a blank slate: no CI/CD pipelines, no deployment infrastructure, barely any monitoring—in short, no software infrastructure at all. This talk shares the key learnings from building a DevOps environment from the ground up. I’ll walk through the essentials: which foundations were laid first, what tools and practices made the difference, and how automation became a daily habit. Through real-world examples, I will demonstrate how pragmatic and incremental steps can jump-start productivity, reduce manual toil, and help teams avoid common pitfalls. https://www.youtube.com/watch?v=RZK9iBGd9rY https://www.youtube-nocookie.com/embed/RZK9iBGd9rY 2026-08-04 yes https://pycon.de/archive/2026/talks/letting-ai-move-robotics-demos-powered-by-python/ 2026-08-04 https://pycon.de/static/media/social/talks/VUHSG9.png Letting AI Move: Robotics Demos Powered by Python https://i.ytimg.com/vi/b9wz31f0WHE/maxresdefault.jpg Letting AI Move: Robotics Demos Powered by Python AI is sometimes hard to explain, especially for people outside of tech. With robots, AI becomes visible and tangible. In this talk we want to show how we can use Python and the huggingface reachy mini as an example to make AI more concrete, interactive, and engaging for beginners and non-experts. https://www.youtube.com/watch?v=b9wz31f0WHE https://www.youtube-nocookie.com/embed/b9wz31f0WHE 2026-08-04 yes https://pycon.de/archive/2026/talks/leveraging-hexagonal-architecture-when-building-applications/ 2026-08-04 https://pycon.de/static/media/social/talks/VY3CY7.png Leveraging Hexagonal Architecture When Building Applications https://i.ytimg.com/vi/8Swb3fqSSo8/maxresdefault.jpg Leveraging Hexagonal Architecture When Building Applications Hexagonal architecture in software development is a design pattern that has existed for more than 20 years and remains highly applicable today as we enter an era where LLMs are increasingly used as development tools. At a high level, it can be used to create distinct layers within an application, resulting in more maintainable and flexible code. One of the primary benefits of utilizing this architecture is the separation of concerns, allowing different components of software to be swapped as needed - whether that is business logic, database technologies, or external services. In this talk, I will discuss the benefits and practical applications of hexagonal architecture. I will also include a detailed walkthrough of how this pattern is implemented in a real-world application. https://www.youtube.com/watch?v=8Swb3fqSSo8 https://www.youtube-nocookie.com/embed/8Swb3fqSSo8 2026-08-04 yes https://pycon.de/archive/2026/talks/lightning-talks-1/ 2026-08-04 https://pycon.de/static/media/social/talks/NXEVSE.png Lightning Talks 1 https://i.ytimg.com/vi/CRi8H2T2TG0/maxresdefault.jpg Lightning Talks 1 Lightning Talks 1 https://www.youtube.com/watch?v=CRi8H2T2TG0 https://www.youtube-nocookie.com/embed/CRi8H2T2TG0 2026-08-04 yes https://pycon.de/archive/2026/talks/lightning-talks-2/ 2026-08-04 https://pycon.de/static/media/social/talks/UFGB7T.png Lightning Talks 2 https://i.ytimg.com/vi/LIN7YTvdCgI/maxresdefault.jpg Lightning Talks 2 Lightning Talks 2 https://www.youtube.com/watch?v=LIN7YTvdCgI https://www.youtube-nocookie.com/embed/LIN7YTvdCgI 2026-08-04 yes https://pycon.de/archive/2026/talks/making-bad-clis-fun-with-small-language-models/ 2026-08-04 https://pycon.de/static/media/social/talks/GMNE3E.png Making bad CLIs fun with Small Language Models https://i.ytimg.com/vi/tNAOyH1DS6o/maxresdefault.jpg Making bad CLIs fun with Small Language Models Command Line Interfaces (CLIs) offer an efficient and powerful way to interact with software, but poorly designed interfaces can be incredibly frustrating. Complicated parameter names and unconventional formats can turn using a great tool into a burdensome experience. Large Language Models (LLMs) seem like a great solution to this problem as they can easily add a natural-language interface to any CLI. However, LLMs can introduce their own challenges, such as requiring API keys or high-performance GPUs. In this talk, I'll demonstrate a method for creating natural-language interfaces for any CLI using fine-tuned Small Language Models. These models are lightweight enough to be run directly on laptops or even smartphones. We'll explore the process of generating synthetic data, fine-tuning models, and evaluating their performance using both an in-house CLI and a well-known open-source package as examples. https://www.youtube.com/watch?v=tNAOyH1DS6o https://www.youtube-nocookie.com/embed/tNAOyH1DS6o 2026-08-04 yes https://pycon.de/archive/2026/talks/making-my-apache-sparktm-talk-more-interesting-using-ai/ 2026-08-04 https://pycon.de/static/media/social/talks/KBGXKC.png Making my Apache Spark™ talk more interesting using AI https://i.ytimg.com/vi/0dZU2qzoFhA/maxresdefault.jpg Making my Apache Spark™ talk more interesting using AI Writing talks is hard, but being a good conference speaker is even harder. Resultantly, this talk is recursive: I'll take a talk previously written for a London data science meetup on using Apache Spark and Apache Kafka to build ML data processing pipelines, and revamp it using Snowflake's Cortex Code CLI! https://www.youtube.com/watch?v=0dZU2qzoFhA https://www.youtube-nocookie.com/embed/0dZU2qzoFhA 2026-08-04 yes https://pycon.de/archive/2026/talks/making-tech-tutorials-accessible-practical-techniques-for-educators/ 2026-08-04 https://pycon.de/static/media/social/talks/GYBRVN.png Making Tech Tutorials Accessible: Practical Techniques for Educators https://i.ytimg.com/vi/GVXXom66MOM/maxresdefault.jpg Making Tech Tutorials Accessible: Practical Techniques for Educators Want to make your tech tutorials accessible but don't know where to start? This talk shares practical techniques anyone can use. In June 2025, I started creating tutorials for deaf and hard-of-hearing learners because my partner is hard of hearing. I learned that accessible content helps everyone: international learners, people on noisy trains, junior developers and tired seniors at the end of the day. In this talk, I will share practical techniques for creating accessible tech tutorials: • Creating videos with meaningful subtitles (manual timing, simple language) • Principles of simple language for technical content • Structuring content so everyone can navigate it easily I am a content creator who learned these techniques through experimentation while teaching Excel. The talk presents my actual workflow with examples from creating tutorials for deaf/hard-of-hearing learners. Whether you're creating video tutorials, writing documentation, or teaching workshops, you'll leave with actionable steps to make your content more accessible. Why it matters: Tech education is growing globally. Making our content accessible isn't just good ethics—it makes our teaching better for everyone. https://www.youtube.com/watch?v=GVXXom66MOM https://www.youtube-nocookie.com/embed/GVXXom66MOM 2026-08-04 yes https://pycon.de/archive/2026/talks/mastering-the-hex-a-case-study-in-reinforcement-learning-for-strategy-games/ 2026-08-25 https://pycon.de/static/media/social/talks/MJTQEJ.png Mastering the Hex: A Case Study in Reinforcement Learning for Strategy Games https://i.ytimg.com/vi/Cp2KOlwDix8/maxresdefault.jpg Mastering the Hex: A Case Study in Reinforcement Learning for Strategy Games What does it take to build an AI that learns to play strategy games from scratch? Over the past year, I chose to explore this question out of personal fascination with game AI — as a seminar project for college, but really as a hobby. The result was a complete reinforcement learning environment for Antiyoy, a turn-based strategy game played on hexagonal grids. The journey raised intriguing challenges: How do you represent hexagonal game boards for neural networks? What do you do when your AI has over 4,000 possible actions to choose from? How do you design rewards that teach strategy rather than just reward flailing in the right direction? This talk shares how these problems were approached using Python's modern ML ecosystem—Gymnasium, PyTorch, and PPO training—ultimately producing an agent that wins nine out of ten games against a random opponent. Whether that qualifies as "strategic play" is a question the agent and I still disagree on. Whether you're curious about building custom RL environments, interested in game AI, or just wondering what reinforcement learning actually looks like when it half-works, you'll leave with practical insights and a healthy dose of realistic expectations. https://www.youtube.com/watch?v=Cp2KOlwDix8 https://www.youtube-nocookie.com/embed/Cp2KOlwDix8 2026-08-25 yes https://pycon.de/archive/2026/talks/metashade-compilerless-immediate-mode-shader-generation-in-pure-python/ 2026-08-04 https://pycon.de/static/media/social/talks/B8GQ9Z.png Metashade: Compilerless Immediate-Mode Shader Generation in Pure Python https://i.ytimg.com/vi/6yQmEvqMDCQ/maxresdefault.jpg Metashade: Compilerless Immediate-Mode Shader Generation in Pure Python Discover how to build a GPU shader generator in pure Python, without having to write a compiler. We start by discussing how Pythonic embedded domain-specific languages (EDSLs) can help address the common challenges of shader programming. We then examine the architectural decisions shared by popular frameworks like Warp and Taichi and outline their limitations. In particular, their reliance on introspection means supporting only a subset of Python - a language within a language - while compiler-like backends necessitate complex implementations in languages like C++. The talk introduces an alternative architecture making it possible to overcome these limitations. Instead of introspection, we capture the program's logic by tracing execution with proxy objects at Python runtime, similar to JAX and PyTorch. Instead of building an IR, we emit target code eagerly, line-by-line, similar to how PyTorch Eager Mode launches computations. And because we don't implement a compiler, the implementation remains 100% Python. Attendees will leave with a toolbox of Python metaprogramming patterns empowering them to write a code generator in Python without having to implement a compiler. https://www.youtube.com/watch?v=6yQmEvqMDCQ https://www.youtube-nocookie.com/embed/6yQmEvqMDCQ 2026-08-04 yes https://pycon.de/archive/2026/talks/no-you-can-t-eval-your-way-to-fairness/ 2026-08-04 https://pycon.de/static/media/social/talks/S7KYEE.png No, you can't 'eval' your way to fairness https://i.ytimg.com/vi/QL4Yg3Xjjxk/maxresdefault.jpg No, you can't 'eval' your way to fairness Fairness is fundamentally not tractable to classic optimisation techniques. It's not a state of the world, it's an experience of it. No technology is fair in a vacuum - fairness can only be understood when a technical system collides with humans. We're seeing a wave of off-the-shelf libraries measuring bad behaviours in LLM outputs, often simplifications of older fairness metrics. They can catch obvious failure modes like slurs. But this is one failure mode among many. Installing a library and calling the job done is fairness washing. The harder, more fruitful approach is to explore the space of failure modes, consider what an ideal world would look like, and design measures, mitigations, and feedback loops accordingly. This is a talk for people who suspect we can't optimise our way to human dignity. https://www.youtube.com/watch?v=QL4Yg3Xjjxk https://www.youtube-nocookie.com/embed/QL4Yg3Xjjxk 2026-08-04 yes https://pycon.de/archive/2026/talks/octopus-automl-extracting-signal-from-small-and-high-dimensional-data/ 2026-08-25 https://pycon.de/static/media/social/talks/8YTYEN.png Octopus AutoML: Extracting Signal from Small and High-Dimensional Data https://i.ytimg.com/vi/YPY0lY6tDvM/maxresdefault.jpg Octopus AutoML: Extracting Signal from Small and High-Dimensional Data Many machine learning tools assume abundant, independent data, rely on a single data split plus cross-validation, and leave test-set separation to the user. In application-driven domains such as industrial materials science and pharmaceutical development, data are scarce, high-dimensional, and often correlated, creating conditions under which standard ML pipelines frequently fail. Small datasets are highly sensitive to the random seed used for splitting, and common pitfalls such as feature selection before splitting or distributing correlated samples across train and test sets cause data leakage and inflated performance metrics. Octopus is an open-source Python AutoML library explicitly designed for small-data, high-dimensional regime. It enforces strict nested cross-validation for model and hyperparameter selection, quantifies performance variability across multiple splits, and tightly controls data leakage. Its modular architecture embeds an internal ML engine, several feature selection methods (e.g., MRMR, Boruta), and external AutoML solutions such as AutoGluon into a unified, rigorous validation framework, enabling systematic and fair comparison of methods on limited data. In addition, Octopus supports survival analysis, addressing time-to-event problems common in healthcare and materials science. This talk will use realistic small-scale datasets to illustrate how conventional pipelines can be misleading and how to obtain more reliable models when every sample matters. https://www.youtube.com/watch?v=YPY0lY6tDvM https://www.youtube-nocookie.com/embed/YPY0lY6tDvM 2026-08-25 yes https://pycon.de/archive/2026/talks/offline-fallback-for-a-mobile-lorawan-gateway/ 2026-08-04 https://pycon.de/static/media/social/talks/7JXYKH.png Offline Fallback for a Mobile LoRaWAN Gateway https://i.ytimg.com/vi/3v23_1w6a4o/maxresdefault.jpg Offline Fallback for a Mobile LoRaWAN Gateway LoRaWAN gateways typically depend on cloud-based network servers, creating a vulnerability during internet outages. This talk presents a hybrid solution: a Raspberry Pi-based mobile gateway that operates on The Things Stack Sandbox while simultaneously decoding all device messages locally. The system leverages existing network infrastructure for broad coverage during normal operation, while maintaining full local data access when connectivity fails. This is particularly valuable for emergency response scenarios and remote monitoring where sensor data must remain available regardless of network conditions. The implementation uses Python for gateway orchestration and API integration, while incorporating existing JavaScript libraries (`lora-packet` and device decoders) for LoRaWAN decryption and payload decoding. Data is stored locally in SQLite for reliability and easy access. https://www.youtube.com/watch?v=3v23_1w6a4o https://www.youtube-nocookie.com/embed/3v23_1w6a4o 2026-08-04 yes https://pycon.de/archive/2026/talks/on-interventional-generalisation/ 2026-08-04 https://pycon.de/static/media/social/talks/BRRHGY.png On Interventional Generalisation https://i.ytimg.com/vi/qpRzSPGeHkE/maxresdefault.jpg On Interventional Generalisation If I do X instead of Y, will I get the outcome I want? What about in a new unseen situation? Making predictions alone is pointless, one wants to act in the world. Furthermore one must act in situations that are similar but different to all past experience. The real underlying goal of all decision making is really interventional generalisation: the ability to evaluate hypothetical choices in new unseen situations. Unfortunately data science and statistics has a inordinate focus on observation and statistical significance instead of intervention, counter-factuals and generalisation. Improve your modelling both practically and conceptually with the mental tools presented in this talk. https://www.youtube.com/watch?v=qpRzSPGeHkE https://www.youtube-nocookie.com/embed/qpRzSPGeHkE 2026-08-04 yes https://pycon.de/archive/2026/talks/open-source-as-a-business-models-paths-and-practice/ 2026-08-04 https://pycon.de/static/media/social/talks/AKGUAC.png Open Source as a Business — Models, Paths, and Practice https://i.ytimg.com/vi/1X43QLZjMso/maxresdefault.jpg Open Source as a Business — Models, Paths, and Practice Open source powers the world's digital infrastructure — from AI research to enterprise data pipelines. But how do you build a sustainable business on it? This panel brings together three figures from the heart of the global open source ecosystem: Yann Lechelle (Probabl), who made the deliberate switch from infrastructure CEO to open source; Sylvain Corlay (QuantStack), whose consulting business is built from and around the core of the Jupyter ecosystem and its maintainers; and Ines Montani (Explosion / spaCy), one of the most influential voices in NLP tooling. Three founders. Three paths. Real answers on what it takes to build — or switch to building — a business in open source. https://www.youtube.com/watch?v=1X43QLZjMso https://www.youtube-nocookie.com/embed/1X43QLZjMso 2026-08-04 yes https://pycon.de/archive/2026/talks/open-table-formats-in-the-wildtm-reloaded-vortexing-ducks-over-floating-icebergs/ 2026-08-04 https://pycon.de/static/media/social/talks/TRGQTL.png Open Table Formats in the Wild™ - Reloaded: Vortexing Ducks over Floating Icebergs https://i.ytimg.com/vi/qujnorOvVA4/maxresdefault.jpg Open Table Formats in the Wild™ - Reloaded: Vortexing Ducks over Floating Icebergs Open table formats have *almost* freed us from vendor lock-in. They form a critical building block of the modern, composable data stack. The most prominent open table format is Apache Iceberg - not only because of its storage layout, but also due to its REST catalog specification. Iceberg has gained significant traction through a recent stream of feature announcements from the community itself, major cloud providers like AWS, and data platform leaders such as Snowflake and Databricks. But cutting through the hype: how does Iceberg actually perform in the real world if you are *not* Netflix or Apple which are capable of *Building Your Own Snowflake* (BYOS)? Can you realistically migrate from legacy solutions to Iceberg and enjoy all its promises without tradeoffs? That, of course, is a rhetorical question. Some even argue that Iceberg got parts of the specification fundamentally wrong!?! Curious? Join me for another episode of Open Table Formats in the Wild™. Expect a practical look at the current state of Apache Iceberg and Apache Parquet, alongside a gentle introduction to DuckLake and Vortex as promising contenders for table and file formats, respectively. https://www.youtube.com/watch?v=qujnorOvVA4 https://www.youtube-nocookie.com/embed/qujnorOvVA4 2026-08-04 yes https://pycon.de/archive/2026/talks/opening-session/ 2026-08-04 https://pycon.de/static/media/social/talks/BQPEUG.png Opening Session https://i.ytimg.com/vi/DWjTeDyZuSo/maxresdefault.jpg Opening Session Opening Session https://www.youtube.com/watch?v=DWjTeDyZuSo https://www.youtube-nocookie.com/embed/DWjTeDyZuSo 2026-08-04 yes https://pycon.de/archive/2026/talks/pair-share-how-formal-mentoring-pushed-rewe-analytics-to-a-new-level/ 2026-08-04 https://pycon.de/static/media/social/talks/HKFCBM.png Pair & Share: How formal Mentoring pushed REWE Analytics to a new level https://i.ytimg.com/vi/1KbCx_j14Cw/maxresdefault.jpg Pair & Share: How formal Mentoring pushed REWE Analytics to a new level As one of Europe’s largest retail corporations, REWE Group owns and manages prominent supermarket chains such as REWE and PENNY, among many other subsidiaries. In this talk I will give a brief overview of how we introduced a formal mentoring program, Pair & Share, at the central analytics department of REWE Group with its more than 150 data scientists, engineers, analysts and other colleagues. Before Pair and Share, there was no formal process for personal, technical or methodological growth. Although there are plenty of possibilities, further training and education was self-organized and fragmented. To increase growth among our colleagues and build and strengthen inter-team exchange, we introduced the formal mentoring program, Pair & Share. This talk will cover a brief overview of REWE Group and our analytics department followed by a motivation for Pair & Share. Afterwards I will explain how we planned the mentoring program and defined the parameters like the matching process, the time frame and how to recruit participants. I will also share my experiences of the first six months of mentoring, what kind of roadblocks but also pleasant surprises we encountered. The talk will be concluded with an outline of how we plan to continue and improve the program. https://www.youtube.com/watch?v=1KbCx_j14Cw https://www.youtube-nocookie.com/embed/1KbCx_j14Cw 2026-08-04 yes https://pycon.de/archive/2026/talks/panel-evolution-revolution-or-illusion-the-future-of-python-and-coding-in-the-age-of-ai/ 2026-08-04 https://pycon.de/static/media/social/talks/WAJQR7.png Panel: Evolution, Revolution, or Illusion? The Future of Python and Coding in the Age of AI https://i.ytimg.com/vi/20w88wM_gQk/maxresdefault.jpg Panel: Evolution, Revolution, or Illusion? The Future of Python and Coding in the Age of AI Software engineering is changing fast. With AI now writing and reasoning about code, does it still make sense to learn Python or any language at all? Is this the evolution of our craft, a true revolution, or just hype from those who benefit most? Join us to debate the future of Python, the risks of AI-driven development, and what skills will actually matter next. https://www.youtube.com/watch?v=20w88wM_gQk https://www.youtube-nocookie.com/embed/20w88wM_gQk 2026-08-04 yes https://pycon.de/archive/2026/talks/panel-what-do-we-still-need-to-learn/ 2026-08-04 https://pycon.de/static/media/social/talks/KFPNUA.png Panel What Do We Still Need to Learn? https://i.ytimg.com/vi/Z7Xlj2eG8sc/maxresdefault.jpg Panel What Do We Still Need to Learn? AI is no longer just a technical tool. It is fundamentally rewriting how we approach every professional task and we are now seeing a shift that impacts every role in every industry. In this panel discussion we will discuss the question no one can answer too confidently: in a world where AI writes the code, drafts the report, and automates the pipeline, what exactly are we still supposed to be learning? https://www.youtube.com/watch?v=Z7Xlj2eG8sc https://www.youtube-nocookie.com/embed/Z7Xlj2eG8sc 2026-08-04 yes https://pycon.de/archive/2026/talks/personalized-restaurant-recommendations-at-scale-combining-transformer-with-gradient-boosted-ranking/ 2026-08-04 https://pycon.de/static/media/social/talks/DVCKHF.png Personalized Restaurant Recommendations at Scale combining Transformer with Gradient-Boosted Ranking https://i.ytimg.com/vi/FbjpwHLaNb4/maxresdefault.jpg Personalized Restaurant Recommendations at Scale combining Transformer with Gradient-Boosted Ranking Wolt’s Universal Venue Ranker (UVR) is a large-scale, sequence-aware ranking model for personalized restaurant recommendations, deployed across more than 30 countries. UVR replaces three previously independent models—Neural Collaborative Filtering, a second-pass ranker, and a first-time-user model—by combining a transformer with a gradient-boosted decision tree for ranking. The model follows a two-stage design. In the first stage, an encoder-style transformer learns a personalized user state representation from historical restaurant purchase sequences enriched with spatiotemporal signals such as time and location. In the second stage, a CatBoostRanker uses the transformer output as an input feature alongside additional user-, venue-, user–venue-, and delivery-specific features to score and rank candidate venues. In this talk, we present the model and service architecture, the training and evaluation setup, and both offline and online results from a multi-country online A/B test, demonstrating significant improvements in global conversion rate and new venue trial rate. We also share practical lessons from deploying and operating a multi-stage ranking model under strict latency constraints at global scale. https://www.youtube.com/watch?v=FbjpwHLaNb4 https://www.youtube-nocookie.com/embed/FbjpwHLaNb4 2026-08-04 yes https://pycon.de/archive/2026/talks/post-processing-and-visualization-of-astrophysical-data-with-pypluto/ 2026-08-04 https://pycon.de/static/media/social/talks/NN7CVP.png Post-Processing and Visualization of Astrophysical Data with PyPLUTO https://i.ytimg.com/vi/VUoxwMPVG8I/maxresdefault.jpg Post-Processing and Visualization of Astrophysical Data with PyPLUTO Modern scientific workflows increasingly rely on interactive analysis, reproducibility, and high-quality visualisation. **PyPLUTO** is a Python package designed to explore, analyse, and visualise numerical simulations produced by the **PLUTO** code for computational astrophysics. This talk shows how *PyPLUTO* leverages the Python ecosystem to transform raw simulation outputs into clear, flexible analysis and visualization workflows. The session demonstrates how domain-specific simulation data can be integrated with tools such as `NumPy` and `Matplotlib` to support efficient post-processing, rapid exploration, and production of publication-quality figures. Attendees will see how structured Python workflows can replace fragmented, ad-hoc scripts, how visualisation accelerates scientific insight, and how Python lowers the barrier between simulation output and interpretation. Although examples are drawn from computational astrophysics, the approach is broadly applicable to any field working with structured simulation data. The talk highlights how lightweight, Python-based post-processing tools can improve clarity, reproducibility, and productivity without imposing heavy frameworks or tightly coupled visualisation pipelines. https://www.youtube.com/watch?v=VUoxwMPVG8I https://www.youtube-nocookie.com/embed/VUoxwMPVG8I 2026-08-04 yes https://pycon.de/archive/2026/talks/practical-refactoring-with-syntax-trees/ 2026-08-04 https://pycon.de/static/media/social/talks/N98BQT.png Practical Refactoring with Syntax Trees https://i.ytimg.com/vi/ocOwCO56Eik/maxresdefault.jpg Practical Refactoring with Syntax Trees The Python Abstract Syntax Tree powers tools like pytest, linters, and automatic refactoring. In this talk, we'll approach syntax trees from first principles and see how Python code can be treated as structured data. We'll then explore how syntax trees can be used to automate refactoring across large codebases. Using a real-world example and the libCST library, we'll build a small refactoring tool and share practical advice for writing and applying automated refactorings. You'll leave with a clear mental model of syntax trees and a solid starting point for writing your own refactoring tools. https://www.youtube.com/watch?v=ocOwCO56Eik https://www.youtube-nocookie.com/embed/ocOwCO56Eik 2026-08-04 yes https://pycon.de/archive/2026/talks/problem-clinic-python-in-regulated-environments-what-works-what-doesn-t-no-video/ 2026-04-12 https://pycon.de/static/media/social/talks/U7QDCH.png Problem Clinic: Python in Regulated Environments --- What Works, What Doesn't [no-video] https://pycon.de/archive/2026/talks/process-analyze-and-transform-python-code-with-asts/ 2026-08-04 https://pycon.de/static/media/social/talks/VWCZXS.png Process, Analyze, and Transform Python Code with ASTs https://i.ytimg.com/vi/f9ZWG9PT9pQ/maxresdefault.jpg Process, Analyze, and Transform Python Code with ASTs You’ve likely used a tool like `black`, `flake8`, or `ruff` to lint or format your code, or a tool like `sphinx` to document it, but you probably do not know how they accomplish their tasks. These tools and many more use **Abstract Syntax Trees (ASTs)** to analyze and extract information from Python code. An AST is a representation of your code's structure that enables you to access and manipulate its different components, which is what makes it possible to automate tasks like code migrations, linting, and docstring extraction. In this workshop, you’ll learn how to use the Python standard library’s `ast` module to parse and analyze code. Using just the standard library, we will implement a couple of common checks from scratch, which will give you an idea of how these tools work and help you build the skills and confidence to use ASTs in your own projects. https://www.youtube.com/watch?v=f9ZWG9PT9pQ https://www.youtube-nocookie.com/embed/f9ZWG9PT9pQ 2026-08-04 yes https://pycon.de/archive/2026/talks/production-ml-across-2015-2035-a-journey-to-the-past-and-the-future/ 2026-08-04 https://pycon.de/static/media/social/talks/PMMEAG.png Production ML across 2015-2035: A Journey to the Past and the Future https://i.ytimg.com/vi/I1GvlW1H4WI/maxresdefault.jpg Production ML across 2015-2035: A Journey to the Past and the Future This talk is an exciting journey that revisits the past decade of Production Machine Learning from 2015 until now, and provides a pragmatic outlook of the next decade towards 2035. We’ll revisit some of the cornerstone python projects that served as the foundation of the "messy innovation" boom (feature stores, orchestration, model serving, monitoring), as well as how it transitioned towards the LLMOps era shifting the stack from training-centric to inference-centric. We will also provide a pragmatic set of predictions for the next decade of MLOps, including some of the trends in ML monitoring, agentic systems and beyond - this will provide actionable guidance to all practitioners to ensure we stay ahead of the curve on the expected skills and domains required to thrive in the near future to come. https://www.youtube.com/watch?v=I1GvlW1H4WI https://www.youtube-nocookie.com/embed/I1GvlW1H4WI 2026-08-04 yes https://pycon.de/archive/2026/talks/programming-quantum-networks-in-python/ 2026-08-04 https://pycon.de/static/media/social/talks/NF7MKB.png Programming Quantum Networks in Python https://i.ytimg.com/vi/MfhIvmbCzPM/maxresdefault.jpg Programming Quantum Networks in Python Quantum networks connect quantum devices including quantum computers, enabling applications not realizable in classical networks, such as secure quantum computing in the cloud and quantum key distribution. These networks are now moving from theory to reality, and as part of the Quantum Internet Alliance, we are actively building a prototype quantum network in Europe, driven by applications developed in Python. In this talk, we will introduce quantum networking and demonstrate how to program quantum network applications in Python by walking through the quantum teleportation protocol. We'll conclude by sharing resources so that you can begin experimenting with quantum network programming yourself. No prior quantum experience required. https://www.youtube.com/watch?v=MfhIvmbCzPM https://www.youtube-nocookie.com/embed/MfhIvmbCzPM 2026-08-04 yes https://pycon.de/archive/2026/talks/pyladies-fireside-chat/ 2026-08-25 https://pycon.de/static/media/social/talks/QBJRBJ.png PyLadies Fireside Chat https://i.ytimg.com/vi/aMebGZmkgnI/maxresdefault.jpg PyLadies Fireside Chat What does it mean to build with Python when AI is reshaping everything? Join Dawn Gibson Wages, Jessica Greene, and host Tereza Iofciu for an honest conversation about Python, local AI, and the craft of being a developer today. https://www.youtube.com/watch?v=aMebGZmkgnI https://www.youtube-nocookie.com/embed/aMebGZmkgnI 2026-08-25 yes https://pycon.de/archive/2026/talks/pytest-tips-and-tricks-for-a-better-testsuite/ 2026-08-04 https://pycon.de/static/media/social/talks/GCGLPN.png pytest tips and tricks for a better testsuite https://i.ytimg.com/vi/RnMbwqJQcFE/maxresdefault.jpg pytest tips and tricks for a better testsuite pytest lets you write simple tests fast - but also scales to very complex scenarios: Beyond the basics of no-boilerplate test functions, this training will show various intermediate/advanced features, as well as gems and tricks. To attend this training, you should already be familiar with the pytest basics (e.g. writing test functions, parametrize, or what a fixture is) and want to learn how to take the next step to improve your test suites. If you're already familiar with things like fixture caching scopes, autouse, or using the built-in `tmp_path`/`monkeypatch`/... fixtures: There will probably be some slides about concepts you already know, but there are also various little hidden tricks and gems I'll be showing. https://www.youtube.com/watch?v=RnMbwqJQcFE https://www.youtube-nocookie.com/embed/RnMbwqJQcFE 2026-08-04 yes https://pycon.de/archive/2026/talks/python-hates-being-pid-1-writing-container-aware-code-for-kubernetes/ 2026-08-04 https://pycon.de/static/media/social/talks/JJDCW3.png Python Hates Being PID 1: Writing Container-Aware Code for Kubernetes https://i.ytimg.com/vi/ASO6-HaDXeY/maxresdefault.jpg Python Hates Being PID 1: Writing Container-Aware Code for Kubernetes On Kubernetes, your Python app runs in a hostile environment, fighting for resources in a straitjacket, bombarded with signals, and being killed and ruthlessly dragged back to life time and again. This is in stark contrast to the wonderful weather of a Linux web server or the blissful utopia of localhost. If not hardened properly, your Python app will find the burden of being containerized too hard to bear. And the result? Zombies! Whether you are a Kubernetes expert, or you just deployed your first containerized Hello World, we will together explore how the Python Interpreter, the Linux Kernel and Kubernetes interact with each other. We will uncover why Python struggles as an init process, how Kubernetes CPU-limits fight the Global Interpreter Lock (GIL) and why Python’s Garbage Collector cannot save you from sudden OOM kills. Most importantly, we will see how to identify, debug, and avoid containerized Python pitfalls. The goal of this talk is to help you stop treating your container like a server and learn to write Cloud-Native Python that knows exactly where it lives. https://www.youtube.com/watch?v=ASO6-HaDXeY https://www.youtube-nocookie.com/embed/ASO6-HaDXeY 2026-08-04 yes https://pycon.de/archive/2026/talks/python-in-climate-tech-vehicle-to-grid/ 2026-08-04 https://pycon.de/static/media/social/talks/QVLTKD.png Python in Climate Tech: Vehicle-to-Grid https://i.ytimg.com/vi/meJu96TGw5Y/maxresdefault.jpg Python in Climate Tech: Vehicle-to-Grid This talk dives into how Python helps us to bridge the gap between automotive and energy industries. Learn how Python helps in enabling Vehicle-to-Grid and therefore the bi-directional integration of EV batteries into the power grid, enabling further use and growth of renewable energies, stabilizing power grids and enhancing the accessibility of electric mobility. https://www.youtube.com/watch?v=meJu96TGw5Y https://www.youtube-nocookie.com/embed/meJu96TGw5Y 2026-08-04 yes https://pycon.de/archive/2026/talks/pytorch-and-cpu-gpu-synchronizations/ 2026-08-04 https://pycon.de/static/media/social/talks/RNT9FV.png PyTorch and CPU-GPU Synchronizations https://i.ytimg.com/vi/dKZxUcuOb8A/maxresdefault.jpg PyTorch and CPU-GPU Synchronizations CPU–GPU synchronizations are a subtle performance killer in PyTorch: they block the host, prevent the CPU from running ahead, and create GPU idle gaps. This talk explains what host-device synchronization is, how it’s triggered by subtle code patterns (dynamic-shapes), and how to diagnose it with NVIDIA Nsight Systems by correlating utilization gaps with long CUDA API calls. We’ll end with practical mitigation patterns, including unit testing for syncs via `torch.cuda.set_sync_debug_mode()` and when a small Triton kernel can help avoid syncs and fuse ops. https://www.youtube.com/watch?v=dKZxUcuOb8A https://www.youtube-nocookie.com/embed/dKZxUcuOb8A 2026-08-04 yes https://pycon.de/archive/2026/talks/reaching-the-next-level-of-abstraction-meta-classes-and-what-they-enable/ 2026-08-04 https://pycon.de/static/media/social/talks/EE39VN.png Reaching the next level of abstraction: meta classes and what they enable https://i.ytimg.com/vi/IeQAARsJiWc/maxresdefault.jpg Reaching the next level of abstraction: meta classes and what they enable Python is especially powerful due to its deep meta programming capabilities. In this talk, I give an overview of one example: meta classes. I show how you can use them to customize class creation, ensure data integrity, or define your own syntactic sugar for classes. https://www.youtube.com/watch?v=IeQAARsJiWc https://www.youtube-nocookie.com/embed/IeQAARsJiWc 2026-08-04 yes https://pycon.de/archive/2026/talks/rediscovering-single-node-processing-when-does-it-make-sense-to-move-from-spark-to-polars/ 2026-08-04 https://pycon.de/static/media/social/talks/HQBC7R.png Rediscovering single-node processing: When does it make sense to move from Spark to Polars? https://i.ytimg.com/vi/gy4_2CwQpPQ/maxresdefault.jpg Rediscovering single-node processing: When does it make sense to move from Spark to Polars? As data engineers, we are used to spinning up a Spark Cluster every time we want to do data processing and handle the overhead that comes with using such a mighty framework. But is this really necessary? In this talk I will argue that single-node processing with Polars is in many cases easier and cheaper. I will compare a typical ETL & Feature Engineering task in Spark and in Polars and offer a pragmatic opinion on when to use one or the other. https://www.youtube.com/watch?v=gy4_2CwQpPQ https://www.youtube-nocookie.com/embed/gy4_2CwQpPQ 2026-08-04 yes https://pycon.de/archive/2026/talks/restaurants-around-train-stations-are-bad-and-i-can-prove-it/ 2026-08-04 https://pycon.de/static/media/social/talks/NAHX3L.png Restaurants around train stations are bad and I can prove it https://i.ytimg.com/vi/ZvEIqSvvat8/maxresdefault.jpg Restaurants around train stations are bad and I can prove it Have you ever asked yourself: Why is there no good food option close to this main station? This talk tries to find out if this is a systematic problem - using publicly available data and Google APIs. After this talk, you will know about the best- and worst-rated restaurants close to main stations in Germany, if kebabs or pizza places are systematically a better choice, and which station is the worst to eat in all of Germany. https://www.youtube.com/watch?v=ZvEIqSvvat8 https://www.youtube-nocookie.com/embed/ZvEIqSvvat8 2026-08-04 yes https://pycon.de/archive/2026/talks/roll-for-architecture-dungeonpy-a-d-d-companion-as-server-thin-clients/ 2026-08-04 https://pycon.de/static/media/social/talks/S9VSCV.png Roll for Architecture: DungeonPy – A D&D Companion as Server + Thin Clients https://i.ytimg.com/vi/FuGsgWy_6-4/maxresdefault.jpg Roll for Architecture: DungeonPy – A D&D Companion as Server + Thin Clients ### **DungeonPy** – an interactive Dungeon&Dragons app for remote campaigns As a matter of fact, tabletop RPGs are secretly distributed systems: one canonical world state, many clients, lossy links (players), and strict access control (“no peeking at the DM notes”). This talk introduces **DungeonPy**, which evolves a Python D&D companion from two local app – a Pygame battle map and a PySimpleGUI initiative/condition tracker – connected by lightweight TCP messages, into an authoritative server with multiple role-aware clients. The result is a fully real-time interactive setup, where the DM controls the full state and can reveal information selectively – under the hood it’s all about client intents, server validation, state updates, event broadcasting and periodic snapshots. We will cover protocol design (deltas vs snapshots, ordering/idempotency), server-side view projections (DM omniscience vs per-player truth and fog-of-war), UI-safe concurrency, and testing your homemade message bus without summoning race conditions. Expect patterns you can reuse in any stateful client/server app – just with more goblins. https://www.youtube.com/watch?v=FuGsgWy_6-4 https://www.youtube-nocookie.com/embed/FuGsgWy_6-4 2026-08-04 yes https://pycon.de/archive/2026/talks/scaling-data-processing-for-training-workloads-at-deepl-research-with-rust/ 2026-08-04 https://pycon.de/static/media/social/talks/F79RG9.png Scaling Data Processing for Training Workloads at DeepL Research with Rust https://i.ytimg.com/vi/7YmaBitI4aY/maxresdefault.jpg Scaling Data Processing for Training Workloads at DeepL Research with Rust This talk will detail how we used Rust to solve a number of resource utilization inefficiencies while scaling data pre-processing to a petabyte scale and enable next-generation model training at DeepL. Besides other factors, this was done by developing an internal library for interacting with Parquet files in a memory efficient nature. Topics include: • Convincing you to love Rust for its memory safety • Comparing C++ and Rust ecosystems for Python library development • Diving into Python-Rust interoperability • Convincing you to love Rust for its user-friendly (yes, actually!) language features • Providing a high-level overview of the continuously growing impact that Rust is having on the Arrow and data engineering ecosystem https://www.youtube.com/watch?v=7YmaBitI4aY https://www.youtube-nocookie.com/embed/7YmaBitI4aY 2026-08-04 yes https://pycon.de/archive/2026/talks/schema-driven-lambdaliths-in-python-with-aws-lambda-powertools-and-pydantic/ 2026-08-04 https://pycon.de/static/media/social/talks/Q9HMT3.png Schema-Driven Lambdaliths in Python with AWS Lambda Powertools and Pydantic https://i.ytimg.com/vi/21AaYamSn4Q/maxresdefault.jpg Schema-Driven Lambdaliths in Python with AWS Lambda Powertools and Pydantic Modern web frameworks such as Hono have renewed interest in schema-driven development and the “Lambdalith” architecture, where an application is delivered as a single AWS Lambda function. While this model provides a predictable developer experience, Python-based serverless systems often struggle to achieve the same consistency, validation, and maintainability in production. Deploying Python web frameworks to AWS Lambda frequently requires additional execution layers—such as ASGI adapters or container-based runtimes—which add complexity and blur data boundaries. For teams that prefer clear, minimal Lambda handlers, these abstractions can hinder both development and operations. This session shares production-proven patterns for building schema-driven Lambdalith applications in Python using AWS Lambda Powertools and Pydantic, without relying on heavy framework abstractions. Through real-world examples, we show how these tools simplify handler logic, standardize request and response validation, and improve observability and error handling. Attendees will leave with practical techniques for building reliable and maintainable Python Lambdalith systems, and insights they can immediately apply to modernizing existing serverless codebases or delivering new production services with confidence. https://www.youtube.com/watch?v=21AaYamSn4Q https://www.youtube-nocookie.com/embed/21AaYamSn4Q 2026-08-04 yes https://pycon.de/archive/2026/talks/securing-ai-agentic-systems-enforcing-safety-constraints-in-ai-agent/ 2026-08-04 https://pycon.de/static/media/social/talks/9PBYAP.png Securing AI Agentic Systems: Enforcing Safety Constraints in AI Agent https://i.ytimg.com/vi/cReCcT1qUqg/maxresdefault.jpg Securing AI Agentic Systems: Enforcing Safety Constraints in AI Agent AI agents are increasingly deployed with autonomy: calling tools, accessing data, modifying systems, and making decisions without human supervision. While prompts and guardrails are often presented as safety solutions, they break down quickly in real-world agentic systems. In this talk, we explore how to enforce safety constraints in AI agents beyond prompting, using engineering techniques familiar to Python developers and data engineers. We will examine common failure modes in agentic systems such as tool misuse, goal drift, and over-permissioning and show how to mitigate them using policy layers, capability boundaries, and execution-time validation. https://www.youtube.com/watch?v=cReCcT1qUqg https://www.youtube-nocookie.com/embed/cReCcT1qUqg 2026-08-04 yes https://pycon.de/archive/2026/talks/sentinel-values-in-python-semantics-double-dispatch-and-the-limits-of-typing/ 2026-08-04 https://pycon.de/static/media/social/talks/88TTRY.png Sentinel Values in Python: Semantics, Double Dispatch, and the Limits of Typing https://i.ytimg.com/vi/AjrxxGJNBQY/maxresdefault.jpg Sentinel Values in Python: Semantics, Double Dispatch, and the Limits of Typing Python relies heavily on special values such as `None`, `NotImplemented`, `Ellipsis`, and `dataclasses.MISSING`. These values are not incidental: they encode language semantics, enable control flow between objects, and shape API design. This talk examines sentinel values as a first-class concept in Python. We will look at why None is often the wrong representation for absence, how NotImplemented enables double dispatch in rich comparisons, and where sentinel values appear throughout the standard library. A central focus is typing. While sentinel values are ubiquitous at runtime, Python currently has no standardized way to express them precisely in type hints. We will examine why Optional, overloads, and Literal fall short, what limited narrowing is possible today, and why creating a “real” custom sentinel with reliable type narrowing is still unsolved. Finally, we will discuss [PEP 661](https://peps.python.org/pep-0661/), i.e., the deferred proposal to standardize sentinel values and their typing semantics, and what its deferral means in practice. Using real-world examples, including Pydantic’s experimental missing concept, this talk provides a clear mental model for sentinel values and realistic guidance for using them in typed Python codebases today. https://www.youtube.com/watch?v=AjrxxGJNBQY https://www.youtube-nocookie.com/embed/AjrxxGJNBQY 2026-08-04 yes https://pycon.de/archive/2026/talks/ship-data-with-confidence-declarative-validation-for-pyspark-pandas/ 2026-08-04 https://pycon.de/static/media/social/talks/93SXWY.png Ship Data with Confidence: Declarative Validation for PySpark & Pandas https://i.ytimg.com/vi/YG_GXTubZVY/maxresdefault.jpg Ship Data with Confidence: Declarative Validation for PySpark & Pandas Tired of data quality issues crashing your PySpark and Pandas pipelines? This talk introduces [dataframe-expectations](https://github.com/getyourguide/dataframe-expectations), a lightweight, open-source library for declarative data validation. We will dive into the library's design and demonstrate how to easily define and apply data quality expectations to catch errors early, reduce debugging time, and ship more reliable data products, faster. Learn to build more robust data pipelines and move from reactive problem-solving to proactive data validation. https://www.youtube.com/watch?v=YG_GXTubZVY https://www.youtube-nocookie.com/embed/YG_GXTubZVY 2026-08-04 yes https://pycon.de/archive/2026/talks/simplicity-scales-rewriting-to-a-django-monolith-and-monorepo/ 2026-08-04 https://pycon.de/static/media/social/talks/AAY8KQ.png Simplicity Scales: Rewriting to a Django Monolith and Monorepo https://i.ytimg.com/vi/5n0KnmVw52A/maxresdefault.jpg Simplicity Scales: Rewriting to a Django Monolith and Monorepo Simplicity scales better than complexity. In this talk we share what we learned from a year-long refactor of our Python-based infrastructure where we majorly improved developer velocity and overall developer happiness with two choices: moving everything into a monorepo and replacing our microservices architecture with a Django monolith. Instead of going deep on any single technology, we offer a holistic view of how these decisions enabled a multi-disciplinary team to move faster on a shared codebase. We'll introduce a blueprint for a uv-based Python monorepo, discuss why we chose "boring" tools over custom solutions, and share the metrics we used to measure success. The metrics dashboard will be open-sourced as part of this talk. https://www.youtube.com/watch?v=5n0KnmVw52A https://www.youtube-nocookie.com/embed/5n0KnmVw52A 2026-08-04 yes https://pycon.de/archive/2026/talks/simplifying-rag-document-pipelines-with-multimodal-embeddings/ 2026-08-04 https://pycon.de/static/media/social/talks/R7TT3E.png Simplifying RAG Document Pipelines with Multimodal Embeddings https://i.ytimg.com/vi/CiPqCzqWvro/maxresdefault.jpg Simplifying RAG Document Pipelines with Multimodal Embeddings In RAG-based systems, the main challenge is often not tuning the LLM itself, but making documents available in a form that can be retrieved reliably. In enterprise settings, the dominant input format is still PDF, ranging from text-heavy reports to slide decks, scanned documents, and visually dense presentations. Traditional document processing pipelines rely on OCR and layout analysis to extract text, followed by chunking and embedding. While this works well for text-heavy documents, much of the original structure is often lost—especially for presentations, multi-column layouts, and visually driven content. Images, charts, and diagrams typically require separate processing, increasing pipeline complexity and fragility. Recent multi-modal embedding models enable a different approach: embedding entire PDF pages directly as images. This preserves layout, visual hierarchy, and embedded graphics in a single representation and significantly simplifies document ingestion. This talk compares classical OCR-based document processing pipelines with multi-modal page embeddings, drawing on benchmarks conducted on real-world enterprise documents across different models. It highlights where this approach performs well, where its limitations lie, and how to design practical, cost-aware retrieval systems in Python. https://www.youtube.com/watch?v=CiPqCzqWvro https://www.youtube-nocookie.com/embed/CiPqCzqWvro 2026-08-04 yes https://pycon.de/archive/2026/talks/simulating-the-world-using-simpy-a-practical-example/ 2026-08-04 https://pycon.de/static/media/social/talks/WDHTQR.png Simulating the World using SimPy: A practical Example https://i.ytimg.com/vi/zayHuvKVr34/maxresdefault.jpg Simulating the World using SimPy: A practical Example Modern systems are complex - and testing them in real environments is often expensive, risky, or simply not reproducible. Simulation is a practical way to explore behavior under controlled conditions: run scenarios, validate assumptions, inject failures on purpose, and repeat experiments without touching production. In this talk, I build a concrete event-based simulation with `SimPy` to compare `load-balancing algorithms` under different conditions. I’ll show how `SimPy`’s processes and events fit together, how to structure the simulation cleanly, and how to move beyond a one-off demo by making runs reproducible and configurable - using `configuration files` and a simple `command-line interface`. https://www.youtube.com/watch?v=zayHuvKVr34 https://www.youtube-nocookie.com/embed/zayHuvKVr34 2026-08-04 yes https://pycon.de/archive/2026/talks/small-language-models-for-tool-calling-are-better-than-you-think/ 2026-08-04 https://pycon.de/static/media/social/talks/AZ7GD3.png Small Language Models for Tool Calling Are Better Than You Think https://i.ytimg.com/vi/nlTn6-kyyFI/maxresdefault.jpg Small Language Models for Tool Calling Are Better Than You Think Large language models have been widely used in tool-calling workflows thanks to their strong performance in generating appropriate function calls. However, due to their size and cost, they are inaccessible to small-scale builders, and server-side computing makes data privacy challenging. Small language models (SLMs) are a promising, affordable alternative that can run on local hardware, ensuring higher privacy. Unfortunately, SLMs struggle with this task - they pass wrong arguments when calling functions with many parameters, and make mistakes when the conversation spans multiple turns. On the other hand, for production applications with specific API sets, we often don't need general-purpose LLMs - we need reliable, specialized models. This talk demonstrates how to increase the accuracy of SLMs (under 8B parameters) for custom tool calling tasks. We will share how leveraging knowledge distillation helps to get the most out of SLMs in low-data settings - they can even outperform LLMs! We will present the whole pipeline from data generation, fine-tuning, and local deployment. https://www.youtube.com/watch?v=nlTn6-kyyFI https://www.youtube-nocookie.com/embed/nlTn6-kyyFI 2026-08-04 yes https://pycon.de/archive/2026/talks/solving-marketplace-cold-start-at-scale-with-ranking/ 2026-08-04 https://pycon.de/static/media/social/talks/GVHZW9.png Solving Marketplace Cold Start at Scale with Ranking https://i.ytimg.com/vi/xS5xM2ojrH0/maxresdefault.jpg Solving Marketplace Cold Start at Scale with Ranking Cold start is a critical bottleneck for marketplaces: new items lack behavioral signals and reviews, so ranking models under-expose them, delaying the very signals needed to rank them well. This talk shares practical solutions developed at scale for a travel marketplace, including guaranteed exposure at key positions, efficient real-time re-ranking, and targeted boosting for unactivated items. Attendees will learn how experiment-driven iteration shaped a robust system that accelerates early traction for new items without sacrificing overall marketplace health. https://www.youtube.com/watch?v=xS5xM2ojrH0 https://www.youtube-nocookie.com/embed/xS5xM2ojrH0 2026-08-04 yes https://pycon.de/archive/2026/talks/sql-is-dead-long-live-sql-engineering-reliable-analytics-agent-from-scratch/ 2026-08-04 https://pycon.de/static/media/social/talks/AF9DNH.png SQL is Dead, Long Live SQL: Engineering reliable analytics agent from scratch https://i.ytimg.com/vi/XvsrRUqyTpg/maxresdefault.jpg SQL is Dead, Long Live SQL: Engineering reliable analytics agent from scratch Is it still worth learning SQL in 2026, or can we just "chat" with our data? This hands-on tutorial explores that exact question by pushing Text-to-SQL to its absolute limits. This won't be just happy paths; we will deliberately expose where LLMs fail : ambiguity, hallucinations, and "dirty" data...and build the engineering stack required to fix them! You will build a local data Agent from scratch using DuckDB, MCP and a minimalist semantic layer. By the end, you will understand the hard boundaries of AI reasoning, how a semantic layer acts as a safety net, and why knowing SQL is still (since 1974) the most critical skill for building reliable analytics agents. https://www.youtube.com/watch?v=XvsrRUqyTpg https://www.youtube-nocookie.com/embed/XvsrRUqyTpg 2026-08-04 yes https://pycon.de/archive/2026/talks/start-ups-investors/ 2026-08-04 https://pycon.de/static/media/social/talks/CNNUZC.png Start-Ups & Investors https://i.ytimg.com/vi/IsmqhGPIwd8/maxresdefault.jpg Start-Ups & Investors Starting a company doesn't require a garage or an MBA — it takes a real problem, a strong team, and someone willing to turn a project into a product. This panel brings together founders, investors, and startup builders from academia, corporate careers, and venture capital for an honest conversation about what it really takes to build a startup in AI, software, and open source. At PyCon DE & PyData, we're surrounded by people solving real problems with code and data every day. This session is for anyone who's ever wondered: could my project become a product? The answer might surprise you. https://www.youtube.com/watch?v=IsmqhGPIwd8 https://www.youtube-nocookie.com/embed/IsmqhGPIwd8 2026-08-04 yes https://pycon.de/archive/2026/talks/state-of-in-browser-ml-webassembly-webgpu-and-the-modern-stack/ 2026-08-04 https://pycon.de/static/media/social/talks/DQDEES.png State of In-Browser ML: WebAssembly, WebGPU, and the Modern Stack https://i.ytimg.com/vi/1_r3GqS7GK8/maxresdefault.jpg State of In-Browser ML: WebAssembly, WebGPU, and the Modern Stack What if you could run real data/ML workflows right in your browsers - sandboxed, with no installation or sending your data anywhere? Such an approach would have tons of benefits: it is easy to distribute, safer by default, and can scale almost infinitely with virtually no infrastructure costs. This talk is a pragmatic overview of the current in-browser ML stack. We’ll cover what workflows are realistic today (from training of traditional ML models to on-device LLM inference), how packaging/loading works, and the constraints one should be aware of. By the end of the talk you will have a clear sense of when in-browser ML is a good fit, and when it isn’t. https://www.youtube.com/watch?v=1_r3GqS7GK8 https://www.youtube-nocookie.com/embed/1_r3GqS7GK8 2026-08-04 yes https://pycon.de/archive/2026/talks/stop-waiting-start-shipping-real-world-strategy-for-open-source-llms/ 2026-08-04 https://pycon.de/static/media/social/talks/9CYWET.png Stop Waiting, Start Shipping: Real-World Strategy for Open-Source LLMs https://i.ytimg.com/vi/Zfva3EEhfbs/maxresdefault.jpg Stop Waiting, Start Shipping: Real-World Strategy for Open-Source LLMs Chinese and American open-source LLMs are competing head-to-head — from DeepSeek and Qwen to Llama and Mistral. The model landscape is broader than ever, yet in Germany the debate still circles around waiting for the next breakthrough. Alexander Hendorf and Sebastian Raschka discuss what these models can and cannot do today, what biases to watch for, and which deployment strategies actually work in practice. The session reserves substantial time for questions and discussion with the audience. https://www.youtube.com/watch?v=Zfva3EEhfbs https://www.youtube-nocookie.com/embed/Zfva3EEhfbs 2026-08-04 yes https://pycon.de/archive/2026/talks/surviving-ai-fatigue-staying-sane-and-relevant-in-a-fast-moving-field/ 2026-08-04 https://pycon.de/static/media/social/talks/GATMPP.png Surviving AI Fatigue: Staying Sane and Relevant in a Fast Moving Field https://i.ytimg.com/vi/O46R_HbZ10U/maxresdefault.jpg Surviving AI Fatigue: Staying Sane and Relevant in a Fast Moving Field In an era where new AI models, benchmarks, and frameworks emerge daily, many of us feel caught in a relentless cycle of catching up, what is called "AI fatigue". This talk dives into the causes and consequences of that fatigue, from information overload and social media hype to the constant pressure to stay relevant. Drawing on personal experience and community insights, we explore why chasing every new paper or trend often leads to burnout rather than mastery. More importantly, we share practical, evidence-backed strategies to stay informed without losing balance: curating a focused “information diet,” setting clear boundaries, using summarization tools intelligently, maintaining a personal knowledge base, and embracing “JOMO”—the joy of missing out. We also discuss how organizations can combat fatigue structurally by promoting focus, curiosity, and psychological safety. This session is for anyone, from beginners to seasoned professionals, seeking to rediscover genuine curiosity in AI while preserving mental well-being. Attendees will leave with concrete tools, actionable habits, and a renewed sense that it is not only acceptable but healthy to not know everything. https://www.youtube.com/watch?v=O46R_HbZ10U https://www.youtube-nocookie.com/embed/O46R_HbZ10U 2026-08-04 yes https://pycon.de/archive/2026/talks/the-art-of-the-optimal-a-pythonic-approach-to-complex-decision-making/ 2026-08-04 https://pycon.de/static/media/social/talks/JPTTMK.png The Art of the Optimal: A Pythonic Approach to Complex Decision-Making https://i.ytimg.com/vi/-tkOTxTDQUM/maxresdefault.jpg The Art of the Optimal: A Pythonic Approach to Complex Decision-Making As Python developers, we frequently tackle complex decision-making problems by writing custom scripts and heuristic algorithms. While a standard greedy algorithm might provide a quick, intuitive fix, it rarely finds the best possible solution—often leaving significant efficiency, performance, and cost-savings on the table. In this talk, we will explore the untapped power of mathematical optimization. We will start with a classic operations challenge. You will see firsthand how a standard rule-based Python heuristic compares to a mathematical optimization model, and how rigorously defining constraints and objectives can guarantee a globally optimal solution. But optimization isn't just for traditional logistics! We will also bridge the gap to Machine Learning. We will demonstrate how optimization techniques can be utilized as a powerful verification step for ML models, such as calculating the minimum pixel changes required to trick a neural network into a misclassification. While we can only scratch the surface of these vast topics, you will walk away with a fresh perspective on problem-solving. Whether you are automating business operations or building robust ML pipelines, you will learn when to graduate from basic heuristics and start leveraging the "art of the optimal". https://www.youtube.com/watch?v=-tkOTxTDQUM https://www.youtube-nocookie.com/embed/-tkOTxTDQUM 2026-08-04 yes https://pycon.de/archive/2026/talks/the-day-the-agent-started-lying-politely/ 2026-08-04 https://pycon.de/static/media/social/talks/P8Y9TD.png The Day the Agent Started Lying (Politely) https://i.ytimg.com/vi/vZdMN82NMTQ/maxresdefault.jpg The Day the Agent Started Lying (Politely) You deploy an agent to automatically route incoming customer support tickets. At first, it is a clear win: response times improve, customers are happier, and support teams finally get some rest. Then time passes. Nothing crashes. Dashboards stay green. No alerts fire. Yet the agent’s decisions slowly degrade first slightly, then inconsistently, and eventually becoming confidently wrong. This is data drift. LLM-based agents in production operate in constantly changing environments. Products launch, outages happen, terminology evolves, and priorities shift. Unlike traditional ML models, LLMs can produce plausible, well-phrased outputs even when they are incorrect, making these failures difficult to detect. In this talk, we focus on practical techniques for continuously evaluating and monitoring LLM-based agents after deployment. Using a support-ticket routing agent as an example, we examine drift signals such as increasing classification uncertainty, spikes in fallback categories, shifts in embedding distributions, and growing disagreement with historical or human decisions. The emphasis is not on training or prompt tuning, but on operating agents safely over time: detecting silent failures early and knowing when intervention, retraining, or retirement is required before users notice. https://www.youtube.com/watch?v=vZdMN82NMTQ https://www.youtube-nocookie.com/embed/vZdMN82NMTQ 2026-08-04 yes https://pycon.de/archive/2026/talks/the-foundation-model-revolution-for-tabular-data/ 2026-08-04 https://pycon.de/static/media/social/talks/KLN78E.png The foundation model revolution for tabular data https://i.ytimg.com/vi/QvU7w_rwXh8/maxresdefault.jpg The foundation model revolution for tabular data Tabular data, spreadsheets organized in rows and columns, are ubiquitous across healthcare, business and finance. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for thousands of use cases of high societal and commercial value. While gradient-boosted decision trees have dominated tabular data for the past 20 years, we demonstrate that this is rapidly changing, with the foundation model revolution having arrived at tabular data. We will show the methods behind this and their extensions to causality, interpretability and robustness, and demo various agentic extensions. https://www.youtube.com/watch?v=QvU7w_rwXh8 https://www.youtube-nocookie.com/embed/QvU7w_rwXh8 2026-08-04 yes https://pycon.de/archive/2026/talks/the-multimodal-era-of-machine-learning-and-how-python-made-it-possible/ 2026-08-04 https://pycon.de/static/media/social/talks/LPUC9T.png The Multimodal Era of Machine Learning (and How Python Made It Possible) https://i.ytimg.com/vi/IdSJXoj4SOk/maxresdefault.jpg The Multimodal Era of Machine Learning (and How Python Made It Possible) Multimodal learning - systems that combine vision, language, audio, and other sensory inputs—has moved from a niche research topic to a central paradigm in modern machine learning. Today’s most influential models no longer operate on a single modality but instead learn rich representations by combining language with images, videos, sound. This shift has fundamentally changed how we build, train, and evaluate current machine learning systems. Python has played a decisive role in this transformation. Acting as a unifying layer across modalities, Python enabled researchers and practitioners to seamlessly combine computer vision, natural language processing, and speech within a single ecosystem. Python-based frameworks lowered the barriers between research communities, and accelerated the rise of large-scale, weakly supervised, and foundation models. However, this success has also introduced new challenges. The ease of experimentation masks growing issues around scalability, reproducibility, and evaluation. Multimodal systems increasingly depend on complex Python-based stacks whose abstractions can obscure underlying assumptions and costs. ... https://www.youtube.com/watch?v=IdSJXoj4SOk https://www.youtube-nocookie.com/embed/IdSJXoj4SOk 2026-08-04 yes https://pycon.de/archive/2026/talks/tidy-finance-in-practice-how-explicit-assumptions-avoid-bad-investment-strategies/ 2026-08-25 https://pycon.de/static/media/social/talks/XGL37G.png Tidy Finance in Practice: How Explicit Assumptions Avoid Bad Investment Strategies https://i.ytimg.com/vi/QAxh5FpVqT8/maxresdefault.jpg Tidy Finance in Practice: How Explicit Assumptions Avoid Bad Investment Strategies Many investment strategies look convincing because they performed well in the past, but these results are often easy to misread and do not always say much about how the strategy would work in the future. In many cases, strong backtest results come not from real skill or insight, but from hidden rules, unclear data choices, or unrealistic assumptions. In this talk, I show how Tidy Finance principles help make these issues visible and easier to examine. Using clear examples from Tidy Finance with Python, I demonstrate that once assumptions are made explicit, many impressive results no longer hold up. https://www.youtube.com/watch?v=QAxh5FpVqT8 https://www.youtube-nocookie.com/embed/QAxh5FpVqT8 2026-08-25 yes https://pycon.de/archive/2026/talks/to-nest-or-not-to-nest-nested-data-types-in-polars-with-big-data/ 2026-08-04 https://pycon.de/static/media/social/talks/7PNT37.png To nest, or not to nest? Nested data types in Polars with big data https://i.ytimg.com/vi/9gWjqjHEM8g/maxresdefault.jpg To nest, or not to nest? Nested data types in Polars with big data Do you find yourself weighing up the pros and cons of using nested types in the Polars library - pondering whether you should encode your variables in structures using lists, arrays or opt for a flat format without complex hierarchy? This talk focuses on the crucial design choices available, the performance implications, and how this impacts the logic of your queries, as well as code readability, when deciding how to implement your big data pipeline in Polars. The methods available for nested types in Polars have seen some significant additions over the last year, with powerful functionality, such as filtering and aggregation, released in the latest versions of the library. These provide much-needed shortcuts for queries interrogating complex nested structures that previously required sophisticated user-defined functions. It makes the use of nested types much easier and intuitive, but does this mean you should nest your data? Through practical examples you’ll learn some guidelines to help you decide. https://www.youtube.com/watch?v=9gWjqjHEM8g https://www.youtube-nocookie.com/embed/9gWjqjHEM8g 2026-08-04 yes https://pycon.de/archive/2026/talks/tracking-knowledge-diversity-in-llm-generated-responses/ 2026-08-04 https://pycon.de/static/media/social/talks/P7NYXB.png Tracking Knowledge Diversity in LLM-Generated Responses. https://i.ytimg.com/vi/vGvXgpWFS_A/maxresdefault.jpg Tracking Knowledge Diversity in LLM-Generated Responses. As large language models (LLMs)-powered “AI highlights” become the first information people see on the Web, a key question arises: how much variety and perspective do these systems actually deliver for information-seeking queries? Do LLMs offer broader viewpoints than traditional search or Wikipedia pages? Do larger models really produce more diverse answers—or are they all converging on the same language, and framing, raising concerns about “knowledge collapse”? Drawing insights from experiments across LLM families, real-world topics, and hundreds of user-style prompts, this talk introduces an open-source framework for benchmarking and tracking epistemic diversity in LLMs. We focus on practical lessons for data scientists building and evaluating LLM-powered search, summaries, and knowledge systems—where diversity of information actually matters. https://www.youtube.com/watch?v=vGvXgpWFS_A https://www.youtube-nocookie.com/embed/vGvXgpWFS_A 2026-08-04 yes https://pycon.de/archive/2026/talks/ty-mypy-the-new-generation-of-python-type-checking/ 2026-08-04 https://pycon.de/static/media/social/talks/QB7VLW.png Ty mypy: The New Generation of Python Type Checking https://i.ytimg.com/vi/_HJ1K6DA-cY/maxresdefault.jpg Ty mypy: The New Generation of Python Type Checking Python’s static typing ecosystem has long been shaped by mypy, but a new contender has entered the space: ty, a high-performance type checker from Astral that has recently exited alpha. With a focus on speed, modern ergonomics, and tight tooling integration, Ty represents a new direction for Python type checking. In this talk, we’ll explore what ty looks like in practice. We’ll cover its core features, how it behaves on real-world codebases, and what changes when type checking becomes fast enough to run constantly. We’ll also compare ty directly with mypy, highlighting strengths, limitations, and trade-offs teams should understand before adopting it. This session will help Python developers evaluate whether ty is ready for production use today—and what it suggests about the future of Python typing tools. https://www.youtube.com/watch?v=_HJ1K6DA-cY https://www.youtube-nocookie.com/embed/_HJ1K6DA-cY 2026-08-04 yes https://pycon.de/archive/2026/talks/type-errors-for-better-agent-assisted-development/ 2026-08-04 https://pycon.de/static/media/social/talks/8YGQZC.png Type Errors for Better Agent-Assisted Development https://i.ytimg.com/vi/OYJiM5RQm-c/maxresdefault.jpg Type Errors for Better Agent-Assisted Development Type annotations aren't just for humans anymore. As AI coding agents write more Python, type checkers offer something unique: fast, concrete diagnostics about what went wrong and where. In this talk, I explore connecting Pyrefly to Claude Code, feeding type errors back to the agent as it works, and whether this is the missing feedback signal for agentic development. https://www.youtube.com/watch?v=OYJiM5RQm-c https://www.youtube-nocookie.com/embed/OYJiM5RQm-c 2026-08-04 yes https://pycon.de/archive/2026/talks/using-sensor-fusion-and-ml-to-navigate-underground-when-gps-fails/ 2026-08-04 https://pycon.de/static/media/social/talks/LVJXK3.png Using Sensor Fusion and ML to Navigate Underground When GPS Fails https://i.ytimg.com/vi/uyrlnwzsW1U/maxresdefault.jpg Using Sensor Fusion and ML to Navigate Underground When GPS Fails In the twisting vaults of a subway, metro, or U-Bahn, there’s often no reliable cell service, wifi, or GPS. Which means riders had no good way of keeping track of their stops or ETA when underground. After collecting extensive ground truth data, we trained a motion classifier using the phone's accelerometer to identify a moving train. This prediction is fed into a location model that combines it with the train schedule to estimate a location, even when GPS fails. We cover our unique data pipeline, feature engineering, and the optimization for high-scale, offline edge deployment to millions of users. https://www.youtube.com/watch?v=uyrlnwzsW1U https://www.youtube-nocookie.com/embed/uyrlnwzsW1U 2026-08-04 yes https://pycon.de/archive/2026/talks/vibe-nlp-for-applied-nlp/ 2026-08-04 https://pycon.de/static/media/social/talks/AWMRFD.png Vibe NLP for Applied NLP https://i.ytimg.com/vi/4u1hp0LiB-k/maxresdefault.jpg Vibe NLP for Applied NLP One of the hardest parts of applied NLP has always been breaking down complex business problems into machine learning components. It's so hard because it requires domain expertise and reasoning about the specific use case, and it's the one thing technology couldn't fix. But what if we could take some of the learnings from AI-powered coding assistants and apply them to solving real-world NLP problems? In this talk, I'll show how we've built powerful assistants and tools to help developers solve NLP tasks using open-source software, and create modular solutions that are small, fast and fully data-private. https://www.youtube.com/watch?v=4u1hp0LiB-k https://www.youtube-nocookie.com/embed/4u1hp0LiB-k 2026-08-04 yes https://pycon.de/archive/2026/talks/wetterdienst-fast-unified-access-to-open-weather-data-with-polars/ 2026-08-04 https://pycon.de/static/media/social/talks/V8DNCL.png Wetterdienst: Fast, Unified Access to Open Weather Data with Polars https://i.ytimg.com/vi/wegSoXmVYNM/maxresdefault.jpg Wetterdienst: Fast, Unified Access to Open Weather Data with Polars Weather and environmental data power analytics, ML, and operations—but APIs differ wildly and data prep is slow. Wetterdienst is a Python library that provides a unified, Polars‑first interface to multiple weather services (DWD, ECCC, EA, NOAA/NWS, Geosphere Austria, IMGW, Eaufrance, WSV, and more). It standardizes request patterns, returns tidy (long) data, converts to SI units, handles caching, timezones (UTC by default), and retries—so teams can focus on analysis instead of plumbing. This talk introduces Wetterdienst’s provider architecture, core request patterns, performance practices with Polars, and how to integrate via Python, CLI, or its REST API. We’ll walk through real examples (station discovery, parameter selection, timeseries retrieval), exporting to databases, and patterns for robust pipelines in ETL and ML. https://www.youtube.com/watch?v=wegSoXmVYNM https://www.youtube-nocookie.com/embed/wegSoXmVYNM 2026-08-04 yes https://pycon.de/archive/2026/talks/what-breaks-when-automatic-speech-recognition-systems-go-multilingual/ 2026-08-04 https://pycon.de/static/media/social/talks/LYCBNT.png What Breaks When Automatic Speech Recognition Systems Go Multilingual https://i.ytimg.com/vi/8GCnHJDoXRk/maxresdefault.jpg What Breaks When Automatic Speech Recognition Systems Go Multilingual Building machine learning models for audio deepfake detection seems straightforward until datasets span multiple languages, such as Hindi, Korean, Mandarin, and German. In practice, multilingual Automatic Speech Recognition (ASR) systems often fail in production because language-specific acoustic variations and assumptions about the processing pipeline break down at scale. This talk examines the engineering challenges of building a multilingual deepfake detection system using a Python-centric pipeline. It covers practical issues encountered during large-scale audio preprocessing, including memory-efficient data loading, resumable feature-extraction workflows, and validation strategies designed to prevent cross-lingual leakage. The session also shares lessons from deploying a multilingual ASR-based system, with a focus on pipeline structure, evaluation correctness, and operational robustness in real-world settings. https://www.youtube.com/watch?v=8GCnHJDoXRk https://www.youtube-nocookie.com/embed/8GCnHJDoXRk 2026-08-04 yes https://pycon.de/archive/2026/talks/when-llms-are-too-big-building-cost-efficient-high-throughput-ml-systems-for-e-commerce-cataloging/ 2026-08-04 https://pycon.de/static/media/social/talks/GAUNKM.png When LLMs Are Too Big: Building Cost-Efficient High-Throughput ML Systems for E-Commerce Cataloging https://i.ytimg.com/vi/FNyheMv6Vt4/maxresdefault.jpg When LLMs Are Too Big: Building Cost-Efficient High-Throughput ML Systems for E-Commerce Cataloging E-commerce cataloging at idealo operates at extreme scale: 4.5 billion offers from 50,000+ shops across six countries, with peak ingestion rates of 4.8 million offers per minute. While large language models (LLMs) provide strong classification accuracy, they are too slow and costly for billion-scale real-time processing. This talk shows how idealo builds a cost-efficient, high-throughput machine learning system that leverages LLM knowledge without deploying full models in production. We present how knowledge distillation from a large e5 instruction model enables a compact multilingual MiniLM encoder to achieve high accuracy, and how optimized inference runtimes and specialized hardware such as AWS Neuron help meet strict latency and cost requirements. Beyond modeling, we highlight key operational challenges: constructing training datasets from massively imbalanced data, selecting the right encoder architecture from today’s model landscape, and designing a robust MLOps lifecycle with automated data sampling, training, deployment, and monitoring. Attendees will learn practical techniques for scaling ML systems under real-world constraints, how to extract value from LLMs when they are too large to serve directly, and how to transition research prototypes into reliable, high-volume production pipelines. https://www.youtube.com/watch?v=FNyheMv6Vt4 https://www.youtube-nocookie.com/embed/FNyheMv6Vt4 2026-08-04 yes https://pycon.de/archive/2026/talks/when-space-weather-breaks-your-gps-building-an-explainable-early-warning-system/ 2026-08-04 https://pycon.de/static/media/social/talks/99UMEL.png When Space Weather Breaks Your GPS: Building an Explainable Early Warning System https://i.ytimg.com/vi/K7Hl3vXB5wA/maxresdefault.jpg When Space Weather Breaks Your GPS: Building an Explainable Early Warning System Have you ever happened to use GPS and realised that it is not working properly? The Sun could be responsible. In this talk, I present a **real-world machine learning forecasting system** designed to predict a Space Weather phenomenon affecting GNSS accuracy and radio communications. The system is based on **CatBoost** and integrates data from space- and ground-based observations. **SHAP** is used to debug model behaviour and to build trust in model outputs. The talk focuses on **model design and evaluation choices**, showing how interpretability and uncertainty-aware forecasting can be combined in a real-time operational pipeline. https://www.youtube.com/watch?v=K7Hl3vXB5wA https://www.youtube-nocookie.com/embed/K7Hl3vXB5wA 2026-08-04 yes https://pycon.de/archive/2026/talks/why-did-the-model-do-that-debugging-the-ghost-in-the-machine/ 2026-08-04 https://pycon.de/static/media/social/talks/MLUK9M.png Why Did The Model Do That? Debugging the Ghost in the Machine https://i.ytimg.com/vi/VaSzeNJHdSY/maxresdefault.jpg Why Did The Model Do That? Debugging the Ghost in the Machine Why did the model say "No"? In an era where machine learning models increasingly influence high-stake decisions, "trust me" isn't a sufficient explanation. Yet, the logic behind many model decisions remains a black box, often hiding bias and making it difficult to establish trust. In this talk, we move beyond the mystery of the "ghost in the machine" and into practical debugging using a structured *XAI Decision Tree*. Instead of guessing which method to use, we will walk through a logical framework that narrows down the field based on a few critical questions: the type of data you have, the level of model access available, and whether you need to explain a single prediction or the entire system. The audience will leave with a clear path to choosing the right explainable AI (XAI) method - such as SHAP, LIME, or Integrated Gradients - and the corresponding Python framework for their specific use case. This session will cover: - Importance of XAI: Understanding why XAI is crucial using a real-world example - XAI Landscape: An overview of existing XAI methods and how they are related - XAI Decision Tree: How to use the structured XAI decision tree to choose the right explanation method for your use case - Local vs global: A common understanding of local vs global explainability - XAI in Practice: XAI in practice as well as corresponding Python frameworks to use https://www.youtube.com/watch?v=VaSzeNJHdSY https://www.youtube-nocookie.com/embed/VaSzeNJHdSY 2026-08-04 yes https://pycon.de/archive/2026/talks/workshop-what-do-we-still-need-to-learn-no-video/ 2026-04-12 https://pycon.de/static/media/social/talks/7YA98N.png Workshop: What do we still need to learn? [no-video] https://pycon.de/archive/2026/talks/you-are-an-intelligent-business-analyst-how-i-learned-to-talk-to-business/ 2026-08-04 https://pycon.de/static/media/social/talks/LVRLSU.png "You are an intelligent business analyst": how i learned to talk to business https://i.ytimg.com/vi/08z826ZYvKI/maxresdefault.jpg "You are an intelligent business analyst": how i learned to talk to business Developers don’t need to become business analysts, but they do need business skills. This talk shows how learning to communicate with stakeholders, uncover real business needs, and bridge gaps between tech and business can dramatically increase your impact. Learn practical techniques to become a trusted technical partner and deliver solutions that truly matter. https://www.youtube.com/watch?v=08z826ZYvKI https://www.youtube-nocookie.com/embed/08z826ZYvKI 2026-08-04 yes https://pycon.de/archive/2026/talks/your-data-is-leaking-a-hands-on-introduction-to-differential-privacy-with-opendp/ 2026-08-04 https://pycon.de/static/media/social/talks/VJPQCR.png Your Data Is Leaking: A Hands-On Introduction to Differential Privacy with OpenDP https://i.ytimg.com/vi/VT2G-UV5MdI/maxresdefault.jpg Your Data Is Leaking: A Hands-On Introduction to Differential Privacy with OpenDP Data analysis and machine learning often involve sensitive information. But how can we ensure that our analyses and releases do not inadvertently reveal information about the individuals in our data? Traditional approaches such as anonymization or releasing only aggregate statistics have repeatedly proven insufficient. Differential privacy is a mathematical framework that offers provable privacy guarantees while still enabling useful data analysis. In this tutorial, we provide a hands-on introduction to differential privacy, covering key concepts relevant to understanding and applying it in practice. The focus will be on practical implementation rather than underlying theory. Using interactive examples in Python, we will explore the core ideas of differential privacy, highlight its attractive properties and limitations, and demonstrate how to build privacy-preserving analyses using OpenDP, an open-source Python library for differential privacy. Participants will leave equipped to continue exploring differential privacy on their own. Familiarity with the basics of Python programming is helpful, but no prior knowledge of differential privacy is required. https://www.youtube.com/watch?v=VT2G-UV5MdI https://www.youtube-nocookie.com/embed/VT2G-UV5MdI 2026-08-04 yes https://pycon.de/archive/2026/talks/your-first-open-source-contribution-in-python-from-fork-to-pull-request/ 2026-08-04 https://pycon.de/static/media/social/talks/KKCYJN.png Your First Open Source Contribution in Python: From Fork to Pull Request https://i.ytimg.com/vi/39Uc1M9Zk-Y/maxresdefault.jpg Your First Open Source Contribution in Python: From Fork to Pull Request Contributing to open source can feel intimidating, even for experienced Python developers. In this hands-on tutorial, participants will make their first real open source contribution to a Python project, learning the complete workflow from fork to pull request. Using a real-world Python library, attendees will practice reading an unfamiliar codebase, making a small but meaningful change, running tests, and opening a pull request following community standards. The focus is on practical skills, tooling, and confidence — not theory. By the end of the session, participants will understand how to start contributing to Python open source projects and feel prepared to continue contributing beyond the workshop. https://www.youtube.com/watch?v=39Uc1M9Zk-Y https://www.youtube-nocookie.com/embed/39Uc1M9Zk-Y 2026-08-04 yes https://pycon.de/archive/2026/talks/zero-copy-or-zero-speed-the-hidden-overhead-of-pyspark-arrow-synapseml-for-inference/ 2026-08-04 https://pycon.de/static/media/social/talks/TPNBRN.png Zero-Copy or Zero-Speed? The hidden overhead of PySpark, Arrow & SynapseML for inference https://i.ytimg.com/vi/Ijd9NwP5skI/maxresdefault.jpg Zero-Copy or Zero-Speed? The hidden overhead of PySpark, Arrow & SynapseML for inference "Zero-copy" data transfer promises free communication between Spark's JVM and Python workers, but at 6 billion rows daily, the reality is far more complex. This session explores the low-level mechanics of distributed inference, focusing on the serialization bottlenecks. We will conduct an analysis of execution plans generated by `pandas_udf`, `mapInPandas`, and SynapseML. We visualize the true cost of pickling, Arrow record batching, and JNI context switching. Join this deep dive to understand the physics of distributed inference and learn how to tune `spark.sql.execution.arrow.maxRecordsPerBatch` to prevent OOMs without starving the CPU. https://www.youtube.com/watch?v=Ijd9NwP5skI https://www.youtube-nocookie.com/embed/Ijd9NwP5skI 2026-08-04 yes