Conferences / PyData
PyData London 2026
Jun 5–7, 2026 · Convene Sancroft, St. Paul's, London, United Kingdom
- Conference
- PyData Data science & ML
- Dates
- Jun 5, 2026 – Jun 7, 2026
- Venue
- Convene Sancroft, St. Paul's
- Place
- London, United Kingdom
- Format
- In person
- Status
- Past
- Attendance
- —
- Website
- pydata.org
- Recorded
- 0 sponsors · 78 speaker appearances
- Notes
- Tutorials Friday 5 June, talks 6-7 June. The 2026 site (pydata.org/london2026/) returned 404 at research time; details from a June 2026 Wayback snapshot (keynotes Samuel Colvin/Pydantic, Rachel-Lee Nabors/Arize, Jeremiah Lowin/Prefect, Martin O'Reilly/Alan Turing Institute). No sponsor wall on the homepage snapshot and ticket prices were not shown on the archived Tito page, so both are missing. Speakers: all sessions in the pretalx schedule; affiliations parsed from pretalx bios. Sponsorship prices from the archived sponsor page (May 2026).
Ticket prices
No ticket prices recorded
Sponsorship packages
| Package | Price | Valid until | Notes | Source |
|---|---|---|---|---|
| Lanyards | £3,000 | — | A-la-carte add-on (limit 1): logo on lanyard, logo on website, 1 conference pass. | web.archive.org |
| Silver Tier | £4,000 | — | Excl. VAT. Logo on website/banner, job board listing (inclusions not captured in the snapshot). | web.archive.org |
| Gold Tier | £8,000 | — | Excl. VAT. 3 tickets, 2 diversity scholarships, expo booth, attendee list. | web.archive.org |
| Platinum Tier | £12,000 | — | Excl. VAT. 6 tickets, 3 diversity scholarships, expo booth, talk or tutorial, attendee list. | web.archive.org |
| Diamond Tier | £17,000 | — | Excl. VAT. 8 conference tickets, 5 diversity scholarships, expo booth, talk or tutorial, attendee list, prime logo on videos, top billing. | web.archive.org |
Sponsors 0
No sponsors recorded
Speakers and talks 78
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Jeremiah Lowin | Prefect | Keynote | Keynote- Jeremiah Lowin- Build Reasonable Software | — |
| Martin O'Reilly | The Alan Turing Institute | Keynote | Keynote- Martin O'Reilly- LLMs and AI agents demystified | — |
| Rachel Lee Nabors | Arize AI | Keynote | Keynote- Rachel Lee Nabors- The Community Is the Boat | — |
| Samuel Colvin | Pydantic | Keynote | Keynote: Samuel Colvin: Pydantic Monty & Logfire: Wild LLMs, from tool calling to computer use | — |
| Abby Tse | Columbia University | Tutorial | Learn to Unlock Document Intelligence with Open-Source AI | — |
| Adam Hill | ComplyAdvantage | Talk | From Chat-with-PDF to Quiz-Master: Live-Grading RAG with LLM-as-Judge in Python | — |
| Alessandra Costantino | — | Talk | What We Expect from XAI - A scientist’s experience between models and users | — |
| Andrew Igdal | University of Washington | Talk | What Can LLMs Do with Messy Residential Electrification Data? | — |
| Arghyadeep Sarkar | Red Hat | Talk | The Silent Crash: Why Your RAG Evaluation Metrics Are Lying to You | — |
| Arthur Andres | — | Talk | Kafka Streaming, the Pythonic Way | — |
| Austen Wallis | University of Southampton | Talk | Fast-Forward(ing) Models: Accelerating High-Dimensional Inference with AI Emulators | — |
| Benjamin Vincent | PyMC Labs | Talk | Did Your Rollout Actually Work? Measuring Phased Launches with Staggered DiD in Python | — |
| Carol Chen | Red Hat | Tutorial | Learn to Unlock Document Intelligence with Open-Source AI | — |
| Cedric Clyburn | Red Hat | Talk | What Can LLMs Do with Messy Residential Electrification Data? | — |
| Cheuk Ting Ho | JetBrains | Talk | Unconference- Feminist AI | — |
| Cheuk Ting Ho | JetBrains | Tutorial | Do you know how well your model is doing? Evaluate your LLMs | — |
| Chris Fonnesbeck | PyMC Labs | Tutorial | Flexible Statistical Modeling with Bayesian Additive Regression Trees | — |
| Chris Fonnesbeck | PyMC Labs | Talk | PyMC Code Sprint | — |
| Daina Bouquin | Anaconda | Talk | Build your castle, dig your moat: AI sovereignty, provenance and compliance | — |
| Damian Bemben | Ada Mode | Talk | The Clean Energy Graveyard: Using Python & Gemini to Map the UK's Cancelled Renewable's | — |
| Daniele Raimondi | MDPI | Talk | Building a Scientific Taxonomy at Scale with Graph Clustering, Embeddings, and LLMs | — |
| Dmitry Petrov | Microsoft | Talk | From SQL to Python: Building Data Context for Agents and People | — |
| Dragos Crintea | — | Tutorial | Making Databases LLM-Ready: Building Production Semantic Layers with Semantido | — |
| Fei Phoon | The Economist | Tutorial | Observing Agentic AI in Production: MCP Server Tracing with OpenTelemetry and Animal Crossing | — |
| Feichi Lu | MDPI | Talk | Building a Scientific Taxonomy at Scale with Graph Clustering, Embeddings, and LLMs | — |
| Fred O'Loughlin | Climate Policy Radar | Talk | Making tech boring to keep data exciting | — |
| Gabriel Lipnik | Anexia Digital Engineering | Talk | Your ML Pipeline Meets the EU AI Act | — |
| Gergely Daroczi | — | Talk | SELECT instance FROM cloud WHERE workload = ? ORDER BY cost_efficiency | — |
| Hitendri Bomble | Red Hat | Talk | The Silent Crash: Why Your RAG Evaluation Metrics Are Lying to You | — |
| Ian Thomas | QuantStack | Talk | JupyterLite: run all your code in a web browser using WebAssembly | — |
| Ines Montani | Explosion | Talk | Vibe NLP for Applied NLP | — |
| Ivo Dilov | — | Talk | Bridging Pandas and Polars: The Hidden Costs of Dataframe Interoperability | — |
| Jacob Tomlinson | NVIDIA | Talk | Documenting your open source projects for machines | — |
| James Fielder | — | Talk | How to write a PyData proposal | — |
| Julie Huang | Tesco | Talk | Tesco AI & Data Science: From Recipes to Reality | — |
| Kamlesh Shah | Morgan Stanley | Talk | Columnar Thinking - Designing for high-performance execution with Arrow and Polars | — |
| Kareem Hussein | Tesco | Talk | Tesco AI & Data Science: From Recipes to Reality | — |
| Katrina Riehl | NVIDIA | Tutorial | GPU Algorithm Authoring with CUDA Tile | — |
| Kavit Tolia | — | Talk | Do Multilingual Embeddings Really Share a Semantic Space? Practical Lessons Across Scripts and Languages | — |
| Ken Obata | Lyft | Talk | Beyond Spark MLlib: Deduplicating Common Crawl at Scale | — |
| Kerry Parker | Climate Policy Radar | Talk | Making tech boring to keep data exciting | — |
| Laura Summers | Pydantic | Talk | The Human-in-the-Loop is Tired | — |
| Lena Shakurova | ParsLabs | Talk | Evaluating multi-turn conversations: A practical guide to AI Agent evals | — |
| Lipika Ramaswamy | — | Tutorial | From Synthetic Examples to Production Signals: Multimodal Training Data Pipelines with Privacy-Safe Feedback | — |
| Luca Baggi | xtream | Talk | Reading the Mind of an LLM | — |
| Maksym Bilychenko | — | Talk | Surviving (and Thriving) as a Data Professional in the Age of AI Agents | — |
| Marco Gorelli | Quansight | Talk | The Polars vs SQL differences nobody is talking about | — |
| Margaritha Groenendijk | — | Talk | The Rules Nobody Writes Down: Decoding and Shifting Team Culture From Any Seat | — |
| Mark Cottam | Climate Policy Radar | Talk | Making tech boring to keep data exciting | — |
| Matt Crooks | BBC | Talk | AI-Assisted Creative for Automated Marketing using Python | — |
| Michel Semaan | — | Talk | Querying the queries: SQL Metaprogramming in Python | — |
| Mingxuan Zhao | IBM | Tutorial | Learn to Unlock Document Intelligence with Open-Source AI | — |
| Nabin Mulepati | NVIDIA | Tutorial | From Synthetic Examples to Production Signals: Multimodal Training Data Pipelines with Privacy-Safe Feedback | — |
| Nathaniel Forde | — | Talk | Hazards on the Causal Path: Bayesian Time-Varying Survival Analysis with PyMC | — |
| Neal Richardson | Posit | Talk | MCP, or not MCP | — |
| Nick Radcliffe | University of Washington | Tutorial | Test-Driven Data Analysis | — |
| Nicolas Makaroff | — | Tutorial | Hands-On with Tabular Foundation Models: From Zero to Strong Baselines | — |
| Niek Tax | Meta | Tutorial | Beyond ML Model Calibration: Hands-On Multicalibration with MCGrad | — |
| NumFOCUS | — | Talk | Diversity Scholar Luncheon | — |
| NumFOCUS | — | Talk | Lightning Talks | — |
| Ono Gantsog | — | Talk | From Noisy Sensors to Events: Event Detection in Sensor data with Kalman Filters and Hidden Markov Models | — |
| Ophelie Bleu | Monzo | Talk | When Your Dataset Has Blind Spots: Practical LLM-Based Data Augmentation | — |
| Oreolorun Olu-Ipinlaye | Crowdhelix | Tutorial | Building a Browser Agent from Scratch: Teach an LLM to Navigate the Web | — |
| Oriol Abril Pla | PyMC Labs | Talk | PyMC Code Sprint | — |
| Oriol Abril Pla | PyMC Labs | Tutorial | Model criticism through posterior predictive checks | — |
| Özge Çinko | ING | Talk | LLM-Based Recommendation Systems: From Embeddings to Real Personalization | — |
| Paddy Mullen | Anaconda | Talk | The Future of Notebooks in a Claude Code World** | — |
| Prattyush Mangal | — | Talk | Production-Ready AI Agents: From LLMs to Small Language Models | — |
| Richard | Partnerize | Tutorial | Building a Browser Agent from Scratch: Teach an LLM to Navigate the Web | — |
| Sam Joseph | DRW | Talk | Python Leadership and Engineering Excellence BoF | — |
| Samuel Jaja | Capgemini | Talk | Building Production Multi-Agent RAG Systems on Serverless AWS | — |
| Simran Dave | University College London | Talk | Mapping the local heat transition: from large-scale geospatial data to real-world impact | — |
| Sofia Pinto | Nesta | Talk | Mapping the local heat transition: from large-scale geospatial data to real-world impact | — |
| Sujee Maniyam | Nebius | Talk | Using coding agents with open models | — |
| Theo van Kraay | Microsoft | Talk | Designing Semantic Memory for Multi-Agent Systems with Python | — |
| Thomas Ogden | Spotify | Talk | No Ropes on a Boat: Coherent Forecasting | — |
| Tun Shwe | Lenses.io | Tutorial | Observing Agentic AI in Production: MCP Server Tracing with OpenTelemetry and Animal Crossing | — |
| Viktor Kessler | Vakamo | Talk | Governance-as-Code for the Lakehouse: Zero Trust with Iceberg REST Catalog and Policy Engines | — |