Conferences / PyData
PyData Amsterdam 2026
Sep 10–12, 2026 · NDSM Loods (conference, 10-11 Sep); Xebia, Wibautstraat 200 (tutorial day, 12 Sep), Amsterdam, Netherlands
- Conference
- PyData Data science & ML
- Dates
- Sep 10, 2026 – Sep 12, 2026
- Venue
- NDSM Loods (conference, 10-11 Sep); Xebia, Wibautstraat 200 (tutorial day, 12 Sep)
- Place
- Amsterdam, Netherlands
- Format
- In person
- Status
- Past
- Attendance
- —
- Website
- amsterdam.pydata.org
- Recorded
- 15 sponsors · 76 speaker appearances
- Notes
- Prices are those shown on the pretix shop when checked after the event (likely the final/regular tier; early-bird prices not captured). Tutorial Day sold separately and sponsored/hosted by Xebia. Speakers: full list from the pretalx schedule export; affiliations parsed from pretalx speaker bios (null where the bio states no clear current employer/institution). Matt Topol's affiliation corrected from 'Apache Arrow' to 'Columnar': his pretalx speaker bio for this edition (https://pretalx.com/pydata-amsterdam2026/speaker/ZC9TDY/) describes him as co-founder of Columnar. Sponsorship prices from the NumFOCUS PyData Amsterdam 2026 prospectus (dated Feb 2026).
Ticket prices
Sponsorship packages
| Package | Price | Valid until | Notes | Source |
|---|---|---|---|---|
| Silver | €3,000 | — | 1 conference pass; logo on website and sponsor banner. | amsterdam.pydata.org |
| Gold | €9,000 | — | 3 conference passes; logo on website, job board listing. | amsterdam.pydata.org |
| Platinum-X | €11,000 | — | 5 conference passes plus listed benefits (see prospectus grid). | amsterdam.pydata.org |
| Platinum | €17,000 | — | Expo booth, sponsored talk, 7 passes, 10% off more. | amsterdam.pydata.org |
| Platinum Premium | €19,000 | — | Either exclusive Tutorial Day sponsor (90-minute tutorial, no main-conference booth) or 15 m2 prime booth; 7 passes, 12% off more. | amsterdam.pydata.org |
| Diamond | €23,000 | — | Limit 2. 18 m2 expo booth, sponsored talk, logo on videos, attendee list access, 10 conference passes, 15% off more. | amsterdam.pydata.org |
Sponsors 15
Title / headline 1
- Adyen · Diamond Sponsor
Platinum 10
- JetBrains · Platinum Premium Sponsor
- KickstartAI · Platinum Premium Sponsor
- Manychat · Platinum Sponsor
- Nebius · Platinum X Sponsor
- Nominal · Platinum Sponsor
- Picnic · Platinum Sponsor
- Posit · Platinum Sponsor
- Sopra Steria · Platinum Sponsor
- Wonderful · Platinum X Sponsor
- Xebia · Platinum Premium Sponsor
Gold 4
- DuckDB Labs · Gold Sponsor
- Polars · Gold Sponsor
- Team Rockstars IT · Gold Sponsor
- TomTom · Gold Sponsor
Speakers and talks 76
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Andy Kitchen | — | Keynote | Mothering the Machine | — |
| Christophe Blefari | — | Keynote | Analytics future | — |
| Jay Alammar | Cohere | Keynote | From LLMs to Agents and from words to actions | — |
| Laura Summers | Pydantic | Keynote | Mothering the Machine | — |
| Maarten Grootendorst | Keynote | From LLMs to Agents and from words to actions | — | |
| Nadieh Bremer | — | Keynote | Amidst the Visualization and Art of Data | — |
| Akhila Vangara | Adyen | Talk | The Context Trap: Addressing Item Neglect and Calibration in Deep Point-Wise Rankers | — |
| Alex Litvinov | Manychat | Talk | Answers you can question: building a verifiable AI analytics agent | — |
| Alexander Kern | Clockworks | Talk | Data First, Model Second: Three Strategies for Production Computer Vision | — |
| Anders Bogsnes | Nordea | Tutorial | Taking Flight: Zero-Copy Data Transfer at Scale with Apache Arrow Flight and Friends | — |
| Anna Pillar | Sogeti | Talk | When Should AI Speak? Letting Agents Decide When It's Their Turn | — |
| Arda Kaygan | — | Talk | Techie vs Comic Episode 3: Natural Intelligence | — |
| Auxten Wang | — | Talk | No GIL, Real Gains: Porting a C++ Python Extension to Free-Threaded Python 3.14 | — |
| Azamat Omuraliev | Databricks | Talk | When RAG is not enough: Architecting for 10M+ context windows | — |
| Bauke Brenninkmeijer | Orq.ai | Talk | Evaluating Agents at Scale: From 50 Examples to a Production Flywheel | — |
| Belle Bruinsma | Adyen | Talk | The Context Trap: Addressing Item Neglect and Calibration in Deep Point-Wise Rankers | — |
| Borja Enrique Vilar Martos | — | Talk | Agent-Friendly Data Platforms: Semantic Layers, Tool APIs, and Guardrails for Agentic | — |
| Cheuk Ting Ho | JetBrains | Tutorial | Do you know how well your model is doing? Evaluate your LLMs | — |
| Christiaan Erdbrink | — | Talk | A short tour of forgotten Machine Learning algorithms | — |
| Corné Vriends | Eneco | Talk | Systems for Scale: Architecting a Nationwide Energy Forecasting Platform | — |
| Csanád Bakos | Vinted | Talk | Real-time vs Batch Features for ML: Lessons from Fraud Detection at Scale | — |
| Daria Mustafina | Eneco | Talk | Systems for Scale: Architecting a Nationwide Energy Forecasting Platform | — |
| Dror A. Guldin | Meta | Talk | Your A/B Test Is Leaking: Practical Lessons in Measuring Network Effects | — |
| Emir Can | — | Talk | When Should AI Speak? Letting Agents Decide When It's Their Turn | — |
| Giampaolo Casolla | GetYourGuide | Talk | From Query to Discovery: Building an AI Agent That Helps Travelers Explore | — |
| Graziano Montanaro | Intella | Talk | DuckLake: The Lakehouse That Finally Embraces the Database | — |
| Grégoire Martinon | — | Tutorial | Beyond LLM-as-Judge: Using GLIDE for Reliable, Scalable Evaluation of GenAI Systems | — |
| Guus van der Ham | Clockworks | Talk | Data First, Model Second: Three Strategies for Production Computer Vision | — |
| Iryna Kondrashchenko | DataForce Solutions GmbH | Talk | Deterministic Orchestration for ML Experiments with Coding Agents | — |
| Jeroen Janssens | Posit | Talk | The modern tool builder: CLIs, agents, and PEP 723 | — |
| Jeroen Nelen | KickstartAI | Talk | A/B Testing Plenary Debates in the Dutch Parliament with Multi-Agent AI using LangGraph | — |
| Jodie Burchell | JetBrains | Talk | Reliable, rigorous, wrong: A psychometric view of LLM evals | — |
| Joost van 't Schip | ProRail | Talk | The unreasonable effectiveness of DAS: ML on fiber-optic vibration data for rail monitoring | — |
| Kader Miyanyedi | — | Tutorial | LLM Evaluation in Production: A/B Testing and Observability | — |
| Kai Jeggle | Dexter Energy | Talk | Embed First, Predict Later: Energy forecasting from weather embeddings | — |
| Kexin Fei | Booking.com | Talk | The A/B Testing Blind Spot: Solving the Opt-In Paradox with Randomized Encouragement and DoubleML | — |
| Konstantinos Tsoumas | Mars | Talk | Maybe 3 Minutes, Maybe Chaos – when Conformal Prediction meets my commuting life | — |
| Laura Israel | Adyen | Talk | The Context Trap: Addressing Item Neglect and Calibration in Deep Point-Wise Rankers | — |
| Leonardo Amorim | — | Talk | Beyond the Holdout: Mitigating Censoring Bias with Asymmetric IPW | — |
| Lin Jia | Booking.com | Talk | The A/B Testing Blind Spot: Solving the Opt-In Paradox with Randomized Encouragement and DoubleML | — |
| Lizzy Chanpaibool | — | Talk | Instro: An open-source Python library for interfacing with hardware test equipment | — |
| Luca Baggi | xtream | Talk | Inside the Mind of an LLM | — |
| Luisa Orozco | TNO-GDN | Talk | When one score is not enough: matching real-world groundwater time series at scale | — |
| Majid Hajiheidari | Picnic | Talk | Rebuilding Picnic’s Recipe Recommender | — |
| Marijn Markus | — | Talk | Enhance: Feeding the World with Data through Multi-Objective Optimization | — |
| Martin Iglesias Goyanes | Adyen | Talk | Trillion-Token Pretraining: Building a Foundational Model for payment data | — |
| Matt Topol | Columnar | Talk | DuckDB + ADBC: Faster, Easier Data Analytics | — |
| Merel Groen | Lynxx | Talk | Scheduling at Scale: Building a Railway Timetable Optimizer in Python | — |
| Mohit Kumar | — | Talk | Systems for Scale: Architecting a Nationwide Energy Forecasting Platform | — |
| Nicolai van der Smagt | Nebius Token Factory | Talk | Open Weights, Cloud Scale: Architecture Patterns for Faster and Cheaper Production Agents | — |
| Niels van Galen Last | — | Talk | When Context Breaks: Recursive Language Models with DSPy | — |
| Oleh Kostromin | — | Talk | Deterministic Orchestration for ML Experiments with Coding Agents | — |
| Oz Mendelsohn | Swap | Talk | Distilling LLMs into Classical ML for 5,000+ Classes | — |
| Özge Çinko | ING | Tutorial | LLM Evaluation in Production: A/B Testing and Observability | — |
| Pauline van Nies | Sopra Steria | Talk | Beyond Benchmarks: Optimizing LLMs and Puzzle Agents for Cryptic Crosswords | — |
| Raúl Soutelo Quintela | Adyen | Talk | Trillion-Token Pretraining: Building a Foundational Model for payment data | — |
| Rayan Daod | Atinary Technologies | Talk | Serving Personalized ML at Scale with Evolving Runtimes | — |
| Ricardo Angel Granados Lopez | — | Talk | Grounding AI Agents in Your Data Model | — |
| Ritchie Vink | Polars | Tutorial | Polars Open Source Sprint: Fix Your First Issue or Build Your Own Expression Plugin | — |
| Rutger Lit | Amsterdam Data Collective | Talk | Scaling Two-Way Fixed Effects Models in Python with pyfixest: Lessons from Airline Pricing | — |
| Ryan Marinelli | University of Washington | Tutorial | Stop Early, Decide Smarter: Bayesian Sequential Testing for LLM Benchmarking | — |
| Sadeeq Akintola | Tutorial | Reuniting the two distant cousins: Orchestrating your end-to-end Data Engineering Workflow Leveraging Python in Apache Beam and Apache Airflow | — | |
| Sako Arts | Wonderful NL | Talk | Computer Use Beyond the Demo: Bringing Legacy Systems into the Agentic Era | — |
| Santosh Pingale | — | Talk | Modernizing Spark: Performance Boost without Rewrite | — |
| Schelto Crone | Lynxx | Talk | The unreasonable effectiveness of DAS: ML on fiber-optic vibration data for rail monitoring | — |
| Shehab Amin | LakeSail | Talk | Modernizing Spark: Performance Boost without Rewrite | — |
| Steven Mi | GetYourGuide | Talk | From Query to Discovery: Building an AI Agent That Helps Travelers Explore | — |
| Tara Farzami | — | Talk | When should an AI Agent say "I Don't Know"? Confidence, routing, and multi-turn evaluation in a LLM system | — |
| Theodore Meynard | GetYourGuide | Talk | Cold Start at Scale: Three Years of Experiments in a Travel Marketplace | — |
| Thijs Bressers | — | Talk | Your dashboard is too late: Building real-time KPI alerting systems with Python | — |
| Thijs Nieuwdorp | Radboud University | Talk | The New Polars Engine That Tackles Megabyte to Terabyte Workloads | — |
| Thijs Nieuwdorp | Radboud University | Tutorial | Polars Open Source Sprint: Fix Your First Issue or Build Your Own Expression Plugin | — |
| Thijs Sluijter | University of Amsterdam | Talk | Rebuilding Picnic’s Recipe Recommender | — |
| van Balkom, Patrick | Mars | Talk | AI You Can Bet Your Business On: How Mars' Context-Lake Builds the Trust Bridge Between People and AI | — |
| Willem Feijen | Lynxx | Talk | Scheduling at Scale: Building a Railway Timetable Optimizer in Python | — |
| You are all invited! | — | Talk | Lightning talks | — |