PyData Berlin 2025

State Space Models (SSMs) are powerful tools for time series analysis, widely used in finance, economics, ecology, and engineering. They allow researchers to encode structural behavior into time series models, including trends, seasonality, autoregression, and irregular fluctuations, to name just a few. Many workhorse time series models, including ARIMA, VAR, and ETS, are special cases of the general statespace framework.

In this practical, hands-on tutorial, attendees will learn how to leverage PyMC's new state-space modeling capabilities (pymc_extras.statespace) to build, fit, and interpret Bayesian state space models.

Starting from fundamental concepts, we'll explore several real-world use cases, demonstrating how SSMs help tackle common time series challenges, such as handling missing observations, integrating external regressors, and generating forecasts.

Optimizing user funnels is a common task for data analysts and data scientists. Funnels are not always linear in the real world. often, the next step depends on earlier responses or actions. This results in complex funnels that can be tricky to analyze. I’ll introduce an open-source Python library I developed that analyzes and visualizes non-linear, conditional funnels by utilizing Graphviz and Streamlit. It calculates conversion rates, drop-offs, time spent on each step, and highlights bottlenecks by color. Attendees will learn about how to quickly explore complex user journeys and generate insightful funnel data.

I will share how our team built an end-to-end system to transform raw satellite imagery into analysis-ready datasets for use cases like vegetation monitoring, deforestation detection, and identifying third-party activity. We streamlined the entire pipeline from automated acquisition and cloud storage to preprocessing that ensures spatial, spectral, and temporal consistency. By leveraging Prefect for orchestration, Anyscale Ray for scalable processing, and the open source STAC standard for metadata indexing, we reduced processing times from days to near real-time. We addressed challenges like inconsistent metadata and diverse sensor types, building a flexible system capable of supporting large-scale geospatial analytics and AI workloads.

Digital mapping has long been dominated by commercial providers, creating barriers of cost, complexity, and privacy concerns. This talk introduces Protomaps, an open-source project that reimagines how web maps are delivered and consumed. Using the innovative PMTiles format – a single-file approach to vector tiles – Protomaps eliminates complex server infrastructure while reducing bandwidth usage and improving performance. We'll explore how this technology democratizes cartography by making self-hosted maps accessible without API keys, usage quotas, or recurring costs. The presentation will demonstrate implementations with Leaflet and MapLibre, showcase customization options, and highlight cases where Protomaps enables privacy-conscious, offline-capable mapping solutions. Discover how this technology puts mapping control back in the hands of developers while maintaining the rich experiences modern applications demand.

The visual exploration of large, high-dimensional datasets presents significant challenges in data processing, transfer, and rendering for engineering in various industries. This talk will explore innovative approaches to harnessing massive datasets for early drug discovery, with a focus on interactive visualizations. We will demonstrate how our team at Bayer utilizes a modern tech stack to efficiently navigate and analyze millions of data points in a high-dimensional embedding space. Attendees will gain insights into overcoming performance challenges, optimizing data rendering, and developing user-friendly tools for effective data exploration. We aim to demonstrate how these technologies can transform the way we interact with complex datasets in engineering applications and eventually allow us to find the needle in a multidimensional haystack.

At GetYourGuide, we transformed experimentation from a centralized, closed system into a democratized, self-service platform accessible to all analysts, engineers, and product teams. In this talk, we'll share our journey to empower individuals across the company to define metrics, create dimensions, and easily extend statistical methods. We'll discuss how we built a Python-based Analyzer toolkit enabling standardized, reusable calculations, and how our experimentation platform provides ad-hoc analytical capabilities through a flexible API. Attendees will gain practical insights into creating scalable, maintainable, and user-friendly experimentation infrastructure, along with access to our open-source sequential testing implementation.

Most Python developers reach for Pandas or Polars when working with tabular data—but DuckDB offers a powerful alternative that’s more than just another DataFrame library. In this tutorial, you’ll learn how to use DuckDB as an in-process analytical database: building data pipelines, caching datasets, and running complex queries with SQL—all without leaving Python. We’ll cover common use cases like ETL, lightweight data orchestration, and interactive analytics workflows. You’ll leave with a solid mental model for using DuckDB effectively as the “SQLite for analytics.”

The EU AI Act is already partly in effect which prohibits certain AI systems. After going through the basics, we cover some of the less talked about aspects of the Act, introducing entities involved in its implementation and how many high risk government and law enforcement use cases are excluded!

As machine learning models become more complex, understanding why they make certain predictions is becoming just as important as the predictions themselves. Whether you're dealing with business stakeholders, regulators, or just debugging unexpected results, the ability to explain your model is no longer optional , it's essential.

In this talk, we'll walk through practical tools in the Python ecosystem that help bring transparency to your models, including SHAP, LIME, and Captum. Through hands-on examples, you'll learn how to apply these libraries to real-world models from decision trees to deep neural networks and make sense of what's happening under the hood.

If you've ever struggled to explain your model’s output or justify its decisions, this session will give you a toolkit to build more trustworthy, interpretable systems without sacrificing performance.

Managing who can see or do what with your data is a fundamental challenge, especially as applications and data grow in complexity. Traditional role-based systems often lack the granularity needed for modern data platforms.
Fine-Grained Authorization (FGA) addresses this by controlling access at the individual resource level. In this 90-minute hands-on tutorial, we will explore implementing FGA using OpenFGA, an open-source authorization engine inspired by Google's Zanzibar. Attendees will learn the core concepts of Relationship-Based Access Control (ReBAC) and get practical experience defining authorization models, writing relationship tuples, and performing authorization checks using the OpenFGA Python SDK. Bring your laptop ready to code to learn how to build secure and flexible permission systems for your data applications.

As machine learning models become more accurate and complex, explainability remains essential. Explainability helps not just with trust and transparency but also with generating actionable insights and guiding decision-making. One way of interpreting the model outputs is using SHapley Additive exPlanations (SHAP). In this talk, I will go through the concept of Shapley values and its mathematical intuition and then walk through a few real-world examples for different ML models. Attendees will gain a practical understanding of SHAP's strengths and limitations and how to use it to explain model predictions in their projects effectively.

Consumer choice models are an important part of product innovation and market strategy. In this talk we'll see how they can be used to learn about substitution goods and market shares in competitive markets using PyMC marketing's new consumer choice module.

NiceGUI is a Python-based web UI framework that enables developers to build interactive web applications without using JavaScript. In this talk, I’ll share how my team used NiceGUI to create an internal A/B testing platform entirely in Python. I’ll discuss the key requirements for the platform, why we chose NiceGUI, and how it helped us design the UI, display results, and integrate with the backend. This session will demonstrate how NiceGUI simplifies development, reduces frontend complexity, and speeds up internal tool creation for Python developers.

Traditional budget planners chase the highest predicted return and hope for the best. Bayesian models take the opposite route: they quantify uncertainty first, then let us optimize budgets with that uncertainty fully on display. In this talk we’ll show how posterior distributions become a set of possible futures, and how risk‑aware loss functions convert those probabilities into spend decisions that balance upside with resilience. Whether you lead marketing, finance, or product, you’ll learn a principled workflow for turning probabilistic insight into capital allocation that’s both aggressive and defensible—no black‑box magic, just transparent Bayesian reasoning and disciplined risk management.

Ever tried passing a Polars Dataframe to a data science library and found that it...just works? No errors, no panics, no noticeable overhead, just...results? This is becoming increasingly common in 2025, yet only 2 years ago, it was mostly unheard of. So, what changed? A large part of the answer is: Narwhals.

Narwhals is a lightweight compatibility layer between dataframe libraries which lets your code work seamlessly across Polars, pandas, PySpark, DuckDB, and more! And it's not just a theoretical possibility: with ~30 million monthly downloads and set as a required dependency of Altair, Bokeh, Marimo, Plotly, Shiny, and more, it's clear that it's reshaping the data science landscape. By the end of the talk, you'll understand why writing generic dataframe code was such a headache (and why it isn't anymore), how Narwhals works and how its community operates, and how you can use it in your projects today. The talk will be technical yet accessible and light-hearted.

In this tutorial, you will play several games that can be used to teach machine learning concepts. Each game can be played in big and small groups. Some involve hands- on material such as cards, some others involve electronic app. All games contain one or more concepts from Machine Learning.

As an outcome, you will take away multiple ideas that make complex topics more understandable – and enjoyable. By doing so, we would like to demonstrate that Machine Learning does not require computers, but the core ideas can be exemplified in a clear and memorable way without. We also would like to demonstrate that gamification is not limited to online quiz questions, but offers ways for learners to bond.

We will bring a set of carefully selected games that have been proven in a big classroom setting and contain useful abstractions of linear models, decision trees, LLMs and several other Machine Learning concepts. We also believe that it is probably fun to participate in this tutorial.

Polars is known for its speed, but its elegance comes from its use of expressions. In this talk, we’ll explore how Polars expressions work and why they are key to efficient and elegant data manipulation. Through real-world examples, you’ll learn how to create, expand, and combine expressions in Polars to wrangle data more effectively.

Causal inference techniques are crucial to understanding the impact of actions on outcomes. This talk shares lessons learned from applying these techniques in real-world scenarios where standard methods do not immediately apply. Our key question is: What is the causal impact of wealth planning services on a network of individual’s investments and securities? We'll examine the challenges posed by practical constraints and show how to deal with them before applying standard approaches like staggered difference-in-difference.

This self-contained talk is prepared for general data scientists who want to add causal inference techniques to their toolbox and learn from real-world data challenges.

WebAssembly is reshaping how Python applications can be delivered - allowing fully interactive apps that run directly in the browser, without a traditional backend server. In this talk, I’ll demonstrate how to build reactive, data-driven web apps using Shinylive for Python, combining efficient local storage with Parquet and extending functionality with optional FastAPI cloud services. We’ll explore the benefits and limitations of this architecture, share practical design patterns, and discuss when browser-based Python is the right choice. Attendees will leave with hands-on techniques for creating modern, lightweight, and highly responsive Python data applications.

Using LiteLLM in a Real-World RAG System: What Worked and What Didn’t

LiteLLM provides a unified interface to work with multiple LLM providers—but how well does it hold up in practice? In this talk, I’ll share how we used LiteLLM in a production system to simplify model access and handle token budgets. I’ll outline the benefits, the hidden trade-offs, and the situations where the abstraction helped—or got in the way. This is a practical, developer-focused session on integrating LiteLLM into real workflows, including lessons learned around deployment, limitations, and decision points. If you’re considering LiteLLM, this talk offers a grounded look at using it beyond simple prototypes.

Drawing on experience at Google designing 50+ RAG applications rolled out to millions of users, this talk presents a practical RAG design blueprint. We'll dissect key decision points for building robust knowledge bases (data types, chunking), selecting effective retrieval strategies beyond basic vector search (including Knowledge Graphs and Text2SQL based on query types), and generating meaningful responses tailored to user needs. Attendees will learn reusable patterns and trade-offs essential for building production-ready, scalable RAG systems.

In January the Synthetic Data SDK was introduced and it quickly is gaining traction as becoming the standard Open Source library for creating privacy-preserving synthetic data. In this hands-on tutorial we're going beyond the basics and we'll look at many of the advanced features of the SDK including differential privacy, conditional generation, multi-tables, and fair synthetic data.

This talk presents variational inference as a tool to scale probabilistic models. We describe practical examples with NumPyro and PyMC to demonstrate this method, going through the main concepts and diagnostics. Instead of going heavy into the math, we focus on the code and practical tips to make this work in real industry applications.

Many projects build knowledge graphs with custom schemas but struggle to align them with standard hubs like Wikidata. Manual mapping is tedious and error-prone, while fully automated methods often lack accuracy. This talk introduces wikidata-mapper, a Python tool leveraging Large Language Models (LLMs via DSPy) to suggest semantic mappings between simple YAML ontology schemas and Wikidata identifiers (QIDs/PIDs). We demonstrate its interactive workflow, including confidence-based auto-acceptance, batch suggestion/review modes for scalability, and a novel hierarchy suggestion feature. Learn how this tool combines LLM power with human oversight to efficiently ground custom knowledge representations in Wikidata, using libraries like inquirer, tenacity, and platformdirs. Ideal for KG practitioners, data engineers, and anyone needing to integrate custom schemas with public knowledge bases.

With a focus on healthcare applications where accuracy is non negotiable, this talk highlights challenges and delivers practical insights on building AI agents which query complex biological and scientific data to answer sophisticated questions. Drawing from our experience developing Owkin-K Navigator, a free-to-use AI co-pilot for biological research, I'll share hard-won lessons about combining natural language processing with SQL querying and vector database retrieval to navigate large biomedical knowledge sources, addressing challenges of preventing hallucinations and ensuring proper source attribution.
This session is ideal for data scientists, ML engineers, and anyone interested in applying python and LLM ecosystem to the healthcare domain.

Many industrial and legacy systems still push critical data over TCP streams. Instead of reaching for heavyweight cloud platforms, you can build fast, lean batch pipelines on-prem using Python and DuckDB.

In this talk, you'll learn how to turn raw TCP streams into structured data sets, ready for analysis, all running on-premise. We'll cover key patterns for batch processing, practical architecture examples, and real-world lessons from industrial projects.

If you work with sensor data, logs, or telemetry, and you value simplicity, speed, and control this talk is for you.

How to use LLMs to categorize hundreds of thousands of products into 1,000 categories at scale? Learn about our journey from manual/rule-based methods, via fine-tuned semantic models, to a robust multi-step process which uses embeddings and LLMs via the OpenAI APIs. This talk offers data scientists and AI practitioners learnings and best practices for putting such a complex LLM-based system into production. This includes prompt development, balancing cost vs. accuracy via model selection, testing mult-case vs. single-case prompts, and saving costs by using the OpenAI Batch API and a smart early-stopping approach. We also describe our automation and monitoring in a PySpark environment.

Transform time-consuming document compliance reviews into automated workflows with Generative AI. Through live demonstrations, learn how to build systems that extract policies from unstructured data, analyze the, conduct compliance assessments in minutes instead of weeks. Moreover, in some pipelines, you can already adjust non-compliant components to make the whole system compliant.

Learn practical implementation patterns and ready-to-use templates to modernize your compliance workflows.

How we sustain what we build — and why the future of tech depends on care, not only code.

The last five years have reshaped tech — through a pandemic, economic uncertainty, shifting politics, and the rapid rise of AI. While these changes have opened new opportunities, they’ve also exposed the limits — and harms — of a “move fast and break things” mindset.

This hands-on tutorial will guide participants through building an end-to-end AI agent that translates natural language questions into SQL queries, validates and executes them on live databases, and returns accurate responses. Participants will build a system that intelligently routes between a specialized SQL agent and a ReAct chat agent, implementing RAG for query similarity matching, comprehensive safety validation, and human-in-the-loop confirmation. By the end of this session, attendees will have created a powerful and extensible system they can adapt to their own data sources.

Are your SQL queries becoming tangled webs that are difficult to decipher, debug, and maintain? This talk explores how to write shorter, more debuggable, and extensible SQL code using Pipelined SQL, an alternative syntax where queries are written as a series of orthogonal, understandable steps. We'll survey which databases and query engines currently support pipelined SQL natively or through extensions, and how it can be used on any platform by compiling pipelined SQL to any SQL dialect using open-source tools. A series of real-world examples, comparing traditional and pipelined SQL syntax side by side for a variety of use cases, will show you how to simplify existing code and make complex data transformations intuitive and manageable.

API calls suck! Okay, not all of them. But building your AI features reliant on third party APIs can bring a lot of trouble. In this talk you'll learn how to use web technologies to become more independent.

The global tech ecosystem continues to grow, yet challenges like limited mentorship, a lack of role models, and fragmented community support hinder progress, especially for underrepresented groups. PyLadies offers a powerful model for bridging these gaps. This talk explores how PyLadies chapters worldwide foster technical growth, increase mentorship opportunities, and drive collaboration to create a more inclusive and sustainable global tech community.

In aviation, search isn’t simple—people use abbreviations, slang, and technical terms that make exact matching tricky. We started with just Postgres, aiming for something that worked. Over time, we upgraded: semantic embeddings, reranking. We tackled filter complexity, slow index builds, and embedding updates and much more. Along the way, we learned a lot about making AI search fast, accurate, and actually usable for our users. It’s been a journey—full of turbulence, but worth the landing.

Using AI agents and automation, PyCon DE & PyData volunteers have transformed chaos into streamlined conference ops. From YAML files to LLM-powered assistants, they automate speaker logistics, FAQs, video processing, and more while keeping humans focused on creativity. This case study reveals practical lessons on making AI work in real-world scenarios: structured workflows, validation, and clear context beat hype. Live demos and open-source tools included.

Open source runs on passion, but passion is not a renewable resource. This talk will explore the hidden emotional and social costs of contributing to open-source projects. From burnout to invisibility, we’ll reflect on how the very communities we depend on can unintentionally exhaust the people keeping them alive. Attendees will walk away with practical ways to support contributors, make communities more welcoming, and build open source that truly includes everyone.

Energy infrastructure is vulnerable to damage by erosion or third party interference, which often takes the form of unsanctioned construction. In this talk we discuss our experiences using deep learning algorithms powered by large foundation models to monitor for changes in bi-temporal very-high resolution satellite imagery.

Are you a scientist or a developer looking to understand how to use NumPy to solve computer vision problems?
NumPy is a Python package that provides the multidimensional array object which you can use to solve the lane detection problem in computer vision for self-driving cars or autonomous driving. You can apply non-machine learning techniques using NumPy to find the straight lines on street images. No other external libraries, just python with NumPy.

A practical deep-dive into Azure DevOps pipelines, the Azure CLI, and how to combine pipeline, bicep, and python templates to build a fully automated web app deployment system. Deploying a new proof of concept app within an actual enterprise environment never was faster.