Explore the selected tutorials below, and we'll be sharing the selected talk proposals soon!

PyData Amsterdam 2025, September 24 - 26

Large Language Models (LLMs) are unlocking transformative capabilities — but integrating them into complex, real-world applications remains a major challenge. Simple prompting isn’t enough when dynamic interaction with tools, structured data, and live context is required. This workshop introduces the Model Context Protocol (MCP), an emerging open standard designed to simplify and standardise this integration. Aimed at forward-thinking developers and technologists, this hands-on session will equip participants with practical skills to build intelligent, modular, and extensible LLM-native applications using MCP.

Graph databases give the same importance to relationships as they do to data. Knowledge graphs allows you to uncover insights in your data and efficiently explore the relationships in your data.

Event-Driven AI Agent Workflows with Dapr

Dana Arsovska & Marc Duiker

As AI systems evolve, the need for robust infrastructure increases. Enter Dapr Agents: an open-source framework for creating production-grade AI agent systems. Built on top of the Dapr framework, Dapr Agents empowers developers to build intelligent agents capable of collaborating in complex workflows - leveraging Large Language Models (LLMs), durable state, built-in observability, and resilient execution patterns. This workshop will walk through the framework’s core components and through practical examples demonstrate how it solves real-world challenges.

Meet Docling: The “Pandas” for document AI

Mingxuan Zhao & Michele Dolfi

A workshop session to show you the basics on how to use Docling to enhance document ingestion in your AI workflow.

Graph-based Retrieval-Augmented Generation (GraphRAG) enhances large language models (LLMs) by grounding their responses in structured knowledge graphs, offering more accurate, domain-specific, and explainable outputs. However, many of the graphs used in these pipelines are automatically generated or loosely assembled, and often lack the semantic structure, consistency, and clarity required for reliable grounding. The result is misleading retrieval, vague or incomplete answers, and hallucinations that are difficult to trace or fix.

Listen: A Practical Introduction to Data Sonification

Tomek Roszczynialski & Samuel Janas

Sonification–using sound to represent data–is a niche technique for exploring complex patterns, expanding the sensory dimensions of data analysis, and discovering musical ideas that are otherwise inaccessible.

Retrieval-Augmented Generation (RAG) systems rely heavily on the quality of the retrieval process to generate accurate and contextually relevant outputs. In this 90-minute tutorial, we explore practical techniques to enhance retrieval across three key stages: pre-retrieval, mid-retrieval, and post-retrieval. Participants will learn how to optimize data preparation, query strategies, reranking, and evaluation to significantly improve the performance of RAG systems. A real-world case study will guide attendees through implementing these methods in a complete retrieval workflow.