MLOps & Deployment
Shipping, monitoring, and operating ML systems in production.
176 talks across all editions.
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Zero-Copy or Zero-Speed? The hidden overhead of PySpark, Arrow & SynapseML for inference ▶ Recording 📝 Transcript
Petar Ilijevski · 2026
"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...
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Your Data Is Leaking: A Hands-On Introduction to Differential Privacy with OpenDP ▶ Recording 📝 Transcript
Shlomi Hod, Marcel Neunhoeffer · 2026
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...
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When Space Weather Breaks Your GPS: Building an Explainable Early Warning System ▶ Recording 📝 Transcript
Vincenzo Ventriglia · 2026
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...
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When LLMs Are Too Big: Building Cost-Efficient High-Throughput ML Systems for E-Commerce Cataloging ▶ Recording 📝 Transcript
Tobias Senst, Bastian Wandt · 2026
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)...
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What Breaks When Automatic Speech Recognition Systems Go Multilingual ▶ Recording 📝 Transcript
Rashmi Nagpal · 2026
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...
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Wetterdienst: Fast, Unified Access to Open Weather Data with Polars ▶ Recording 📝 Transcript
Benjamin · 2026
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...
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Using Sensor Fusion and ML to Navigate Underground When GPS Fails ▶ Recording 📝 Transcript
Étienne Tremblay · 2026
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...
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To nest, or not to nest? Nested data types in Polars with big data ▶ Recording 📝 Transcript
Daniel Finnan · 2026
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...
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The Day the Agent Started Lying (Politely) ▶ Recording 📝 Transcript
Asya Melnik · 2026
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....
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The Art of the Optimal: A Pythonic Approach to Complex Decision-Making ▶ Recording 📝 Transcript
Justine Broihan · 2026
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...
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Surviving AI Fatigue: Staying Sane and Relevant in a Fast Moving Field ▶ Recording 📝 Transcript
Ajay, Jeyashree Krishnan · 2026
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...
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Stop Waiting, Start Shipping: Real-World Strategy for Open-Source LLMs ▶ Recording 📝 Transcript
Sebastian Raschka, Alexander CS Hendorf · 2026
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...
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State of In-Browser ML: WebAssembly, WebGPU, and the Modern Stack ▶ Recording 📝 Transcript
Oleh Kostromin, Iryna Kondrashchenko · 2026
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...
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Small Language Models for Tool Calling Are Better Than You Think ▶ Recording 📝 Transcript
Gabi Kadlecova · 2026
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...
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Simplifying RAG Document Pipelines with Multimodal Embeddings ▶ Recording 📝 Transcript
Arne Grobrügge · 2026
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,...
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Ship Data with Confidence: Declarative Validation for PySpark & Pandas ▶ Recording 📝 Transcript
Ryan Sequeira · 2026
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...
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Schema-Driven Lambdaliths in Python with AWS Lambda Powertools and Pydantic ▶ Recording 📝 Transcript
Tanio Toranosuke, Haruto Mori · 2026
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...
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Python Hates Being PID 1: Writing Container-Aware Code for Kubernetes ▶ Recording 📝 Transcript
Kavish Nareshchandra Dahekar · 2026
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...
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Production ML across 2015-2035: A Journey to the Past and the Future ▶ Recording 📝 Transcript
Alejandro Saucedo · 2026
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...
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Post-Processing and Visualization of Astrophysical Data with PyPLUTO ▶ Recording 📝 Transcript
Giancarlo Mattia · 2026
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...
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Panel What Do We Still Need to Learn? ▶ Recording 📝 Transcript
Paula Gonzalez Avalos, Sebastian Unterreitmeier, Silvia Hänig, Dr. Kristian Rother · 2026
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...
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Open Source as a Business — Models, Paths, and Practice ▶ Recording 📝 Transcript
Yann Lechelle, Ines Montani, Sylvain Corlay, Alexander CS Hendorf · 2026
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...
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Offline Fallback for a Mobile LoRaWAN Gateway ▶ Recording 📝 Transcript
Jannis Lübbe · 2026
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...
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Octopus AutoML: Extracting Signal from Small and High-Dimensional Data ▶ Recording 📝 Transcript
Nils Haase, Andreas Wurl · 2026
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...
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Making my Apache Spark™ talk more interesting using AI ▶ Recording 📝 Transcript
Celeste Horgan · 2026
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...
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Learnings Building DevOps as a Software Engineer ▶ Recording 📝 Transcript
Gaweng Tan · 2026
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...
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Kickstart Coding at Scale: How Project Template Automation Unlocks Developer Productivity ▶ Recording 📝 Transcript
Yannik Tausch · 2026
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...
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Is my AI Recruiting biased? - How to evaluate these systems ▶ Recording 📝 Transcript
Sebastian Krauss · 2026
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...
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How to Search Through 800 Billion Records in Real Time ▶ Recording 📝 Transcript
Mirano Tuk, Filip Bacic · 2026
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...
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Holistic Optimization: Implementing "Pipeline-as-a-Trial" HPO with Ray and Cloud Infra ▶ Recording 📝 Transcript
Abdullah Taha · 2026
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?...
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From Struggling to Mastery: A Practical Guide to Data Pipeline Operations ▶ Recording 📝 Transcript
Akif Cakir · 2026
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...
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From Row-Wise to Columnar: Speeding Up PySpark UDFs with Arrow and Polars ▶ Recording 📝 Transcript
Aimilios Tsouvelekakis · 2026
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...
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From Research Models to SLAs: Operationalizing TSFMs with Python ▶ Recording 📝 Transcript
Jeyashree Krishnan, Catarina Filipe · 2026
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...
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From Prompt to Production: How to use AI Code Assistants for Python Data Systems ▶ Recording 📝 Transcript
Serhii Sokolenko · 2026
**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...
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Escape the Hype: Teaching LLM Concepts Through an Interactive AI Factory Game ▶ Recording 📝 Transcript
Vadim Vlasov, Eric Glaser, Lisa Amrhein · 2026
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...
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Empowering Data Scientists with Zero Platform Friction: Deploying Streamlit & Friends in 3 Minutes ▶ Recording 📝 Transcript
Bernhard Schäfer, Nicolas Renkamp · 2026
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...
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Don’t call your LLM too often! How to build your dialog graph with confidence and sleep at night. ▶ Recording 📝 Transcript
Evgeniya Ovchinnikova, Andrei Beliankou · 2026
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...
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Designing and Scaling a Python Library in the Open: Architecture, Automation and Community ▶ Recording 📝 Transcript
Camila Maia · 2026
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...
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Demystifying Containers with Python: Building a Minimal Engine from Scratch ▶ Recording 📝 Transcript
Alexander Zaytsev · 2026
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...
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Causal Inference through the lens of probabilistic programming ▶ Recording 📝 Transcript
Dr. Juan Orduz · 2026
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,...
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Building Trust in Your Data Pipelines with Observability ▶ Recording 📝 Transcript
Stefan Dienst · 2026
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:...
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Building reliable data pipelines with polars and dataframely ▶ Recording 📝 Transcript
Oliver Borchert, Andreas Albert · 2026
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...
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Building Non-Biased Synthetic Datasets: What Actually Works (and What Fails) ▶ Recording 📝 Transcript
Shiva Banasaz Nouri · 2026
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,...
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Beyond Kafka and S3: Python Data Pipelines with HTTP-Native Bytestreams ▶ Recording 📝 Transcript
Johannes Dröge · 2026
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...
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Before You Ship Your Agent: An Agent Builder’s Primer on Jailbreaking Attacks ▶ Recording 📝 Transcript
Simonas Černiauskas · 2026
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,...
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Accuracy Is Overrated: Ship Stable Forecasts (Without Lying to Yourself) ▶ Recording 📝 Transcript
Illia Babounikau · 2026
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...
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A minimalist introduction to Ansible ▶ Recording 📝 Transcript
Raniere Silva · 2026
[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...
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You don’t think about your Streamlit app optimization until you try to deploy it to the cloud ▶ Recording 📝 Transcript
Darya Petrashka · 2025
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...
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Writing reliable software while depending on hazardous APIs ▶ Recording 📝 Transcript
Romain Dorgueil · 2025
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...
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Why E.ON Loves Python ▶ Recording 📝 Transcript
Christer Friberg · 2025
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...
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Using Causal thinking to make Media Mix Modeling ▶ Recording 📝 Transcript
Carlos Trujillo · 2025
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...
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Streamlining the Cosmos: Pythonic Workflow Management for Astronomical Analysis ▶ Recording 📝 Transcript
Raphael Hviding · 2025
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...
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Streamlining Python deployment with Pixi: A Perspective from production ▶ Recording 📝 Transcript
Dennis Weyland · 2025
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...
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Size matters: Inspecting Docker images for Efficiency and Security ▶ Recording 📝 Transcript
Irena Grgic · 2025
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...
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Securing RAG Pipelines with Fine Grained Authorization ▶ Recording 📝 Transcript
Sohan Maheshwar · 2025
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...
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Scraping LEGO for Fun: A Hacky Dive into Dynamic Data Extraction ▶ Recording 📝 Transcript
Peter Lodri · 2025
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...
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Scaling Python: An End-to-End ML Pipeline for ISS Anomaly Detection with Kubeflow ▶ Recording 📝 Transcript
Christian Geier, Henrik Sebastian Steude · 2025
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...
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Scalable Python and SQL Data Engineering without Migraines ▶ Recording 📝 Transcript
Dirk Jung · 2025
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...
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Rustifying Python: A Practical Guide to Achieving High Performance While Maintaining Observability ▶ Recording 📝 Transcript
Max Höhl · 2025
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...
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Quiet on Set: Building an On-Air Sign with Open Source Technologies ▶ Recording 📝 Transcript
Danica Fine · 2025
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...
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Python Performance Unleashed: Essential Optimization Techniques Beyond Libraries ▶ Recording 📝 Transcript
Thomas Berger · 2025
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...
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Pipeline-level differentiable programming for the real world ▶ Recording 📝 Transcript
Alessandro Angioi · 2025
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,...
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Oh, no! Users love my GenAI-Prototype and want to use it more. ▶ Recording 📝 Transcript
Thomas Prexl, Frank Rust · 2025
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...
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Offline Disaster Relief Coordination with OpenStreetMap and FastAPI ▶ Recording 📝 Transcript
Jannis Lübbe · 2025
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...
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Machine Learning Models in a Dynamic Environment ▶ Recording 📝 Transcript
Isabel Drost-Fromm · 2025
"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...
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LLM Inference Arithmetics: the Theory behind Model Serving ▶ Recording 📝 Transcript
Luca Baggi · 2025
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...
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Lessons learned in bringing a RAG chatbot with access to 50k+ diverse documents to production ▶ Recording 📝 Transcript
Bernhard Schäfer, Nico Mohr · 2025
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...
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Langfuse, OpenLIT, and Phoenix: Observability for the GenAI Era ▶ Recording 📝 Transcript
Emanuele Fabbiani · 2025
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...
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Introducing the Synthetic Data SDK - Privacy Preserving Synthetic Data for AI/ML
Michael Platzer · 2025
AI-generated synthetic data is gaining traction as a privacy-safe solution for data access and sharing. This data is created from original datasets, maintaining privacy without compromising utility. In this Session,...
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Instrumenting Python Applications with OpenTelemetry ▶ Recording 📝 Transcript
Mika Naylor, Emily Woods · 2025
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...
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Guardians of the Code: Safeguarding Machine Learning Models in a Climate Tech World ▶ Recording 📝 Transcript
Doreen Sacker · 2025
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...
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Going Global: Taking code from research to operational open ecosystem for AI weather forecasting ▶ Recording 📝 Transcript
Jesper Dramsch · 2025
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...
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GitMLOps – How we are managing 100+ ML pipelines in AWS SageMaker ▶ Recording 📝 Transcript
Bogdan Girman · 2025
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...
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Generative AI Monitoring with PydanticAI and Logfire
Marcelo Trylesinski · 2025
In this talk, we will explore how the integration of PydanticAI and Logfire creates a powerful foundation for generative AI applications. We'll demonstrate how these tools combine to form sophisticated AI workflows...
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From Trees to Transformers: Our Journey Towards Deep Learning for Ranking ▶ Recording 📝 Transcript
Theodore Meynard, Mihail Douhaniaris · 2025
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...
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From Tensors to Clouds — A Practical Guide to Zarr V3 and Zarr-Python 3 ▶ Recording 📝 Transcript
Sanket Verma · 2025
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...
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Filling in the Gaps: When Terraform Falls Short, Python and Typer Step In ▶ Recording 📝 Transcript
Yuliia Barabash · 2025
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...
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Electify - Retrieval-Augmented Generation for Voter Information in the 2024 European Election ▶ Recording 📝 Transcript
Christian Liedl · 2025
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...
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Design, Generate, Deploy: Contract-First with FastAPI ▶ Recording 📝 Transcript
Dr. Evelyne Groen, Kateryna Budzyak · 2025
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...
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Deploying Synchronous and Asynchronous Django Applications for Hobby Projects ▶ Recording 📝 Transcript
melhin · 2025
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...
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Dataframely — A declarative, 🐻❄️-native data frame validation library ▶ Recording 📝 Transcript
Daniel Elsner, Oliver Borchert · 2025
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...
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Data as (Python) Code ▶ Recording 📝 Transcript
Francesco Calcavecchia · 2025
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...
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Conquering PDFs: document understanding beyond plain text ▶ Recording 📝 Transcript
Ines Montani · 2025
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...
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Chasing the Dark Universe with Euclid and Python: Unveiling the Secrets of the Cosmos ▶ Recording 📝 Transcript
Guadalupe Canas Herrera · 2025
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...
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Building versatile operating setups for real world use and testing with Python and the Raspberry Pi ▶ Recording 📝 Transcript
Jens Nie · 2025
**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...
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Building Reliable AI Agents for Publishing: A DSPy-Based Quality Assurance Framework ▶ Recording 📝 Transcript
Simonas Černiauskas · 2025
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...
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Building a Self-Hosted MLOps Platform with Kubernetes ▶ Recording 📝 Transcript
Josef Nagelschmidt · 2025
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...
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Blazing-Fast Python in Your Database: Unlocking Data Science at Scale with Exasol ▶ Recording 📝 Transcript
Alexander Stigsen · 2025
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...
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Beyond DALL-E: Advanced Image Generation Workflows with ComfyUI ▶ Recording 📝 Transcript
René Fa · 2025
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...
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Beyond Alembic and Django Migrations ▶ Recording 📝 Transcript
Rotem Tamir · 2025
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...
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Beyond Agents: What AI Strategy Really Needs in 2025 ▶ Recording 📝 Transcript
Alexander CS Hendorf · 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...
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Whispered Secrets: Building An Open-Source Tool To Live Transcribe & Summarize Conversations ▶ Recording 📝 Transcript
John Sandall · 2024
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...
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Unleashing Confidence in SQL Development through Unit Testing ▶ Recording 📝 Transcript
Tobias Lampert · 2024
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...
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There is a Better Way to Automate and Manage Your (Fluid) Simulations ▶ Recording 📝 Transcript
Julian Wagenschütz · 2024
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...
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The Struggles We Skipped: Data Engineering for the TikTok Generation ▶ Recording 📝 Transcript
Anuun, Hiba Jamal · 2024
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...
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The key to reliability - Testing in the field of ML-Ops ▶ Recording 📝 Transcript
Gunar Maiwald, Tobias Senst · 2024
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....
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The evolution of Feature Stores ▶ Recording 📝 Transcript
Olamilekan Wahab · 2024
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,...
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Streamlining Python Development: A Practical Approach to CI/CD with GitHub Actions ▶ Recording 📝 Transcript
Artem Kislovskiy · 2024
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...
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Moving from Offline to Online Machine Learning with River ▶ Recording 📝 Transcript
Tun Shwe · 2024
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...
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Mostly Harmless Fixed Effects Regression in Python with PyFixest ▶ Recording 📝 Transcript
Alexander Fischer · 2024
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,...
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Missing Data, Bayesian Imputation and People Analytics with PyMC ▶ Recording 📝 Transcript
Nathaniel Forde · 2024
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...
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Lessons learned from deploying Machine Learning in an old-fashioned heavy industry ▶ Recording 📝 Transcript
Robert Meyer · 2024
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...
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Keynote - Ten Key Questions that a Company Should Ask to have Responsible AI ▶ Recording 📝 Transcript
Ricardo Baeza-Yates · 2024
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...
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From idea to production in a day: Leveraging Azure ML and Streamlit to build and user test machine learning ideas quickly ▶ Recording 📝 Transcript
Florian Roscheck · 2024
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...
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Everything you need to know about change-point detection 📝 Transcript
Charles Truong · 2024
Change-point detection is a crucial processing step when dealing with long and non-stationary time series. It has been applied in many contexts, such as human activity recognition, speech/sound processing and...
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Enhance your balcony power plant with Python ▶ Recording 📝 Transcript
Jannis Lübbe · 2024
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...
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Deploying your Python application to Android ▶ Recording 📝 Transcript
Shyamnath Premnadh · 2024
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....
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DDataflow: An open-source end-to-end testing framework for ML pipelines ▶ Recording
Theodore Meynard, Jean Machado · 2024
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...
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Build TikTok's Personalized Real-Time Recommendation System in Python with Hopsworks ▶ Recording 📝 Transcript
Aleksey Veresov · 2024
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...
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Beyond Deployment: Exploring Machine Learning Inference Architectures and Patterns ▶ Recording 📝 Transcript
Tim Elfrink · 2024
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...
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Best of both worlds - How we built an AI-aided content creation tool for language learning ▶ Recording 📝 Transcript
Lea Petters, Hector Hernandez · 2024
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,...
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Advanced Observability with OpenTelemetry and Python ▶ Recording 📝 Transcript
Anton Caceres · 2024
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...
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A Retrieval Augmented Generation system to query the scikit-learn documentation ▶ Recording 📝 Transcript
Guillaume Lemaitre · 2024
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...
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Why GPU Clusters Don't Need to Go Brrr? Leverage Compound Sparsity to Achieve the Fastest Inference Performance on CPUs
Damian Bogunowicz · 2023
Forget specialized hardware. Get GPU-class performance on your commodity CPUs with compound sparsity and sparsity-aware inference execution. This talk will demonstrate the power of compound sparsity for model...
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The State of Production Machine Learning in 2023 ▶ Recording 📝 Transcript
Alejandro Saucedo · 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...
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The Battle of Giants: Causality vs NLP => From Theory to Practice ▶ Recording 📝 Transcript
Aleksander Molak · 2023
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...
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Staying Alert: How to Implement Continuous Testing for Machine Learning Models ▶ Recording 📝 Transcript
Emeli Dral · 2023
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...
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Software Design Pattern for Data Science ▶ Recording 📝 Transcript
Theodore Meynard · 2023
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...
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Shrinking gigabyte sized scikit-learn models for deployment ▶ Recording 📝 Transcript
Pavel Zwerschke, Yasin Tatar · 2023
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...
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Pragmatic ways of using Rust in your data project ▶ Recording 📝 Transcript
Christopher Prohm · 2023
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...
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Postmodern Architecture: The Python Powered Modern Data Stack ▶ Recording 📝 Transcript
John Sandall · 2023
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...
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Observability for Distributed Computing with Dask ▶ Recording 📝 Transcript
Hendrik Makait · 2023
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...
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Most of you don't need Spark. Large-scale data management on a budget with Python ▶ Recording 📝 Transcript
Guillem Borrell · 2023
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...
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MLOps in practice: our journey from batch to real-time inference ▶ Recording 📝 Transcript
Theodore Meynard · 2023
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...
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Maximizing Efficiency and Scalability in Open-Source MLOps: A Step-by-Step Approach ▶ Recording 📝 Transcript
Paul Elvers · 2023
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...
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Machine Learning Lifecycle for NLP Classification in E-Commerce ▶ Recording 📝 Transcript
Gunar Maiwald, Tobias Senst · 2023
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...
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Hyperparameter optimization for the impatient ▶ Recording 📝 Transcript
Martin Wistuba · 2023
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...
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How to build observability into a ML Platform ▶ Recording 📝 Transcript
Alicia Bargar · 2023
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...
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Have your cake and eat it too: Rapid model development and stable, high-performance deployments
Christian Bourjau, Jakub Bachurski · 2023
At the boundary of model development and MLOps lies the balance between the speed of deploying new models and ensuring operational constraints. These include factors like low latency prediction, the absence of...
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From notebook to pipeline in no time with LineaPy ▶ Recording 📝 Transcript
Thomas Fraunholz · 2023
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...
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Fear the mutants. Love the mutants. ▶ Recording 📝 Transcript
Max Kahan · 2023
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...
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Enabling Machine Learning: How to Optimize Infrastructure, Tools and Teams for ML Workflows
Yann Lemonnier · 2023
In this talk, we will explore the role of a machine learning enabler engineer in facilitating the development and deployment of machine learning models. We will discuss best practices for optimizing infrastructure...
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Delivering AI at Scale ▶ Recording 📝 Transcript
Severin Schmitt, Anna Achenbach, Thorsten Kranz · 2023
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...
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Create interactive Jupyter websites with JupyterLite ▶ Recording 📝 Transcript
Jeremy TULOUP, QuantStack · 2023
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...
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Cloud Infrastructure From Python Code: How Far Could We Go? ▶ Recording 📝 Transcript
Etzik Bega, Asher Sterkin · 2023
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...
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Bringing NLP to Production (an end to end story about some multi-language NLP services)
Larissa Haas, Jonathan Brandt · 2023
Models in Natural Language Processing are fun to train but can be difficult to deploy. The size of their models, libraries and necessary files can be challenging, especially in a microservice environment. When...
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Bayesian Marketing Science: Solving Marketing's 3 Biggest Problems ▶ Recording 📝 Transcript
Dr. Thomas Wiecki · 2023
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...
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What I learned from monitoring more than 30 Machine Learning Use Cases ▶ Recording 📝 Transcript
Lina Weichbrodt · 2022
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: -...
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We know what your app did last summer. Do you? Observing Python applications using Prometheus.
Jessica Greene (she/her), Vanessa Aguilar · 2022
When your service is running in production do you know if it’s performing as intended? How can you know if latency for requests is higher than expected, debug memory consumption or get alerted on certain behaviors?...
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The state of DevOps for Python projects ▶ Recording 📝 Transcript
Tobias Heintz · 2022
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...
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Squirrel - Efficient Data Loading for Large-Scale Deep Learning ▶ Recording 📝 Transcript
Dr. Thomas Wollmann · 2022
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...
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Quitting pip: How we use git submodules to manage internal dependencies that require fast iteration
Philipp Stephan · 2022
Everyone knows the pain of dependency management and things become especially complicated when you have to manage internal dependencies across many distinct repositories. After a review of the current state of Python...
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PPML: Machine Learning on Data you cannot see ▶ Recording 📝 Transcript
Valerio Maggio · 2022
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...
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Optimize your network inference time with OpenVINO ▶ Recording 📝 Transcript
Adrian Boguszewski · 2022
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...
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Making MLOps uncool again ▶ Recording 📝 Transcript
David · 2022
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...
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Making Machine Learning Applications Fast and Simple with ONNX
Jan-Benedikt Jagusch, Christian Bourjau · 2022
Taking trained machine learning models from inside a Jupyter notebook and deploying them into a production microservice is painful for two reasons: - Models are not fully self-contained and need to be packaged...
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Machine Learning Testing Ecosystem of Python ▶ Recording 📝 Transcript
Yunus Bulut · 2022
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...
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Introduction to MLOps with MLflow ▶ Recording 📝 Transcript
Tobias Sterbak · 2022
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...
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Forget Mono vs. Multi-Repo - Building Centralized Git Workflows with Python ▶ Recording 📝 Transcript
David Melamed · 2022
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...
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Easily build interactive plots and apps with hvPlot ▶ Recording 📝 Transcript
Philipp Rüdiger, Maxime Liquet · 2022
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...
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Battle of Pipelines - who will win python orchestration in 2022? ▶ Recording 📝 Transcript
Jannis Grönberg · 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...
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5 Steps to Speed Up Your Data-Analysis on a Single Core ▶ Recording 📝 Transcript
Jonathan Striebel · 2022
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...
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Production-level data pipelines that make everyone happy using Kedro
Yetunde Dada · 2019
Learn how easy it is to apply software engineering principles to your data science and data engineering code. Expect an overview of Kedro, a library that implements best practices for data pipelines with an eye...
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Privacy-preserving Machine Learning for text processing ▶ Recording 📝 Transcript
Sarah Diot-Girard · 2019
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...
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Parallel programming for python developers – Let’s Go(lang) ▶ Recording 📝 Transcript
Dominik Henter, Jéssica Lins · 2019
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...
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Kubernetes 101 for Python Developers ▶ Recording 📝 Transcript
Christian Barra · 2019
`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.
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Docker and Python - A Match made in Heaven ▶ Recording 📝 Transcript
Dr. Hendrik Niemeyer · 2019
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.
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Decentralized and Privacy-Preserving ML via TensorFlow Federated ▶ Recording 📝 Transcript
Peter Kairouz, Amlan Chakraborty · 2019
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...
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Build a Machine Learning pipeline with Jupyter and Azure
Daniel Heinze · 2019
With increasing focus on Machine Learning systems in almost every business it is important, to build a great pipeline to train, test and deploy your models. In this session we will show a way to do that with Jupyter and Azure
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Applying deployment oriented mindset for building Machine Learning models
Marianna Diachuk · 2019
Developing ensemble model with hundreds of features? And getting stuck for months trying to deploy the model and fighting with data inconsistency and bugs? This talk will introduce the way to build the development...
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🌈Apache Airflow for beginners ▶ Recording 📝 Transcript
Deleted User · 2019
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.
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Airflow: your ally for automating machine learning and data pipelines
Enrica Pasqua, Bahadir Uyarer · 2019
Now that you finally have your Machine Learning model trained, what’s the next step for moving to production? Orchestrating, scheduling and monitoring ML inference pipelines is a big challenge. Airflow can be your...
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6 Years of Docker: The Good, the Bad and Python Packaging ▶ Recording 📝 Transcript
Sebastian Neubauer · 2019
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.
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Selinon - dynamic distributed task flows ▶ Recording 📝 Transcript
Fridolín Pokorný · 2018
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.
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Measuring the hay in the haystack: quantifying hidden variables using Bayesian Inference
Omer Yuksel · 2018
Monitoring performance metrics in network traffic is important in technology-driven trading, but not every metric is visible to measure. Here I discuss how to tackle this problem via Bayesian inference using PyMC3 package.
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Introduction to Docker for Pythonistas ▶ Recording 📝 Transcript
Jan Wagner · 2018
Docker is a major driver of container virtualization and comes in very handy for your day-to-day work with Python.
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From exploration to deployment - combining PyTorch and TensorFlow for Deep Learning
Marcel Kurovski · 2018
PyTorch vs. TensorFlow? Why not PyTorch + TensorFlow? Lets's combine deep learning frameworks and use their individual strengths for explorative and production-oriented tasks along a data science project.
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Distributed Hyperparameter search with sklearn and kubernetes ▶ Recording 📝 Transcript
Jakob Karalus · 2018
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...
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Beyond Jupyter Notebooks - Building your own Data Science platform with Python & Docker
Joshua Görner · 2018
This talk will interactively show a potential reference architecture for a self-build data science platform using Python & Docker. The presented services tackle common data science pain points such as model...
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Achieving Resilient Code with Integration Tests ▶ Recording 📝 Transcript
Alexandre Figura · 2018
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.
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Synthetic Data for Machine Learning Applications ▶ Recording
Dr. Hendrik Niemeyer · 2017
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...
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Simple Data Engineering in python 3.5+ with Bonobo ▶ Recording
Romain Dorgueil · 2017
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...
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Observing your applications with Sentry and Prometheus ▶ Recording
Patrick Mühlbauer · 2017
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...
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Large-scale machine learning pipelines using Luigi, PySpark and scikit-learn ▶ Recording
Alexander Bauer · 2017
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,...
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Flow is in the Air: Best Practices of Building Analytical Data Pipelines with Apache Airflow ▶ Recording
Dominik Benz · 2017
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...
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Data Plumbing 101 - ETL Pipelines for Everyday Projects ▶ Recording
Eberhard Hansis · 2017
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...