Conferences / PyData
PyData Amsterdam 2023
Sep 14–16, 2023 · de Kromhouthal, Gedempt Hamerkanaal 231, Amsterdam, Amsterdam, Netherlands
- Conference
- PyData Data science & ML
- Dates
- Sep 14, 2023 – Sep 16, 2023
- Venue
- de Kromhouthal, Gedempt Hamerkanaal 231, Amsterdam
- Place
- Amsterdam, Netherlands
- Format
- In person
- Status
- Past
- Attendance
- —
- Website
- amsterdam.pydata.org
- Recorded
- 15 sponsors · 83 speaker appearances
- Notes
- Tickets via Ticket Tailor (talk pass individual/corporate plus separate Tutorial Pass); prices not captured. The Babbel logo is alt-labelled "Adyen" on the page; recorded as Babbel from its link/logo. Speakers from archived snapshots of the (offline) self-hosted pretalx NoJS schedule and speaker list; affiliations parsed from those bios (null where not stated).
Ticket prices
No ticket prices recorded
Sponsors 15
Title / headline 2
- Adyen · Diamond Sponsors
- Dexter Energy · Diamond Sponsors
Gold 9
- Booking.com · Gold Sponsors
- Capgemini · Gold Sponsors
- Coiled · Gold Sponsors
- Datapane · Gold Sponsors
- DuckDB Labs · Gold Sponsors
- Explosion · Gold Sponsors
- IMC · Gold Sponsors
- KickstartAI · Gold Sponsors
- Taipy · Gold Sponsors
Silver 1
- HumanSignal · Silver Sponsors
Speakers and talks 83
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Alessandro Cappelli | — | Keynote | Keynote "Processing billions of tokens for training Large Language Models, tools and knowledge" | — |
| Guilherme Penedo | — | Keynote | Keynote "Processing billions of tokens for training Large Language Models, tools and knowledge" | — |
| Julien Launay | — | Keynote | Keynote "Processing billions of tokens for training Large Language Models, tools and knowledge" | — |
| Katharine Jarmul | Thoughtworks | Keynote | Keynote "AI Without Dystopia" | — |
| Robert G. Erdmann | — | Keynote | Keynote - Python for Imaging and Artificial Intelligence in Cultural Heritage | — |
| Thomas Wolf | Hugging Face | Keynote | Keynote "Processing billions of tokens for training Large Language Models, tools and knowledge" | — |
| Vicki Boykis | Duo Security @ Cisco | Keynote | Keynote "Build and keep your context window" | — |
| Vincent Warmerdam | Explosion | Keynote | Keynote "Natural Intelligence is All You Need [tm]" | — |
| Adrin | — | Talk | Let’s exploit pickle, and `skops` to the rescue! | — |
| Alexander Backus | Dexter Energy | Talk | Harnessing uncertainty: the role of probabilistic time series forecasting in the renewable energy transition | — |
| Alexandre Sajus | — | Talk | Turning your Data/AI algorithms into full web applications in no time with Taipy | — |
| Alon Nir | Spotify | Talk | Power Users, Long Tail Users, and Everything In Between: Choosing Meaningful Metrics and KPIs for Product Strategy | — |
| Alyona Galyeva | Thoughtworks | Talk | Data Contracts in action powered by Python open source ecosystem | — |
| Ana Chaloska | — | Talk | To One-Hot or Not: A guide to feature encoding and when to use what | — |
| Andy Kitchen | — | Talk | Promptly Evaluating Prompts with Bayesian Tournaments | — |
| Arliss Collins | NumFOCUS | Tutorial | Unconference: How to Host a DEI Unconference | — |
| Azamat Omuraliev | ING | Talk | Building true Machine Learning MVPs: Validating the value chain as a product data scientist | — |
| Benjamin Bossan | Hugging Face | Talk | Extend your scikit-learn workflow with Hugging Face and skorch | — |
| Bram van den Akker | Booking.com | Talk | Tables as Code: The Journey from Ad-hoc Scripts to Maintainable ETL Workflows at Booking.com | — |
| Busra Cikla | ING | Talk | Transfer Learning in Boosting Models | — |
| Cheuk Ting Ho | — | Tutorial | Generating Data Frames for your test - using Pandas stratgies in Hypothesis | — |
| Cor Zuurmond | — | Talk | Minimizing the Data Mesh Mess | — |
| Danial Senejohnny | — | Talk | Survival Analysis: a deep dive | — |
| Daniel van der Ende | Xebia | Talk | Return to Data's Inferno: are the 7 layers of data testing hell still relevant? | — |
| Dror A. Guldin | Meta | Talk | Power Users, Long Tail Users, and Everything In Between: Choosing Meaningful Metrics and KPIs for Product Strategy | — |
| Emeli Dral | Evidently AI | Talk | Mind the language: how to monitor NLP and LLM in production | — |
| Fabio Buso | Hopsworks | Talk | MLOps on the fly: Optimizing a feature store with DuckDB and ArrowFlight | — |
| Felipe Moraes | Booking.com | Talk | Enhancing Economic Outcomes: Leveraging Business Metrics for Machine Learning Model Optimization | — |
| Feng Zhao | Adyen | Talk | Graph Neural Networks for Real World Fraud Detection | — |
| Florian Jacta | Groupe Les Mousquetaires | Talk | Turning your Data/AI algorithms into full web applications in no time with Taipy | — |
| Fokko Driesprong | — | Talk | PyIceberg: Tipping your toes into the petabyte data-lake | — |
| Francesco Bruzzesi | HelloFresh | Talk | Bayesian ranking for tennis players in PyMC | — |
| Hadi Abdi Khojasteh | Deltatre | Talk | Distillation Unleashed: Domain Knowledge Transfer with Compact Neural Networks | — |
| Hannes Mühleisen | DuckDB Labs | Talk | In-Process Analytical Data Management with DuckDB | — |
| ildar safilo | — | Talk | Mastering Recommendation Systems Evaluation: An A/B Testing Approach with Insights from the Industry | — |
| Ines Montani | Explosion | Talk | There are no bad labels, only happy accidents | — |
| Jakob Willisch | Babbel | Talk | The proof of the pudding is in the (way of) eating: quasi-experimental methods of causal inference and their practical pitfalls | — |
| James Powell | — | Talk | Cumulative Index Max in pandas | — |
| Jeroen Rombouts | — | Talk | From Vision to Action: Designing and Deploying Effective Computer Vision Pipelines | — |
| Jetze Schuurmans | — | Tutorial | Designing a Machine Learning System | — |
| Jon Smith | Booking.com | Talk | Tables as Code: The Journey from Ad-hoc Scripts to Maintainable ETL Workflows at Booking.com | — |
| Jordi Smit | — | Talk | LLM Agents 101: How I Gave ChatGPT Access to My To-Do List | — |
| Joris Van den Bossche | Voltron Data | Talk | What the PDEP? An overview of some upcoming pandas changes | — |
| Jorrick Sleijster | Adyen | Talk | Achieving developer autonomy on on-premise data clusters using Kubernetes. | — |
| Judith van Stegeren | — | Tutorial | Building a personal search engine with llama-index | — |
| Jurriaan Nagelkerke | — | Talk | Revealing the True Motives of News Readers | — |
| Katharine Jarmul | Thoughtworks | Talk | Encrypted Computation: What if decryption wasn't needed? | — |
| Kemal Tugrul Yesilbek | ABN AMRO | Tutorial | Unconference: Interviews: Tips and Stories from Both Sides | — |
| Kevin Klein | QuantCo | Talk | Causal Inference Libraries: What They Do, What I'd Like Them To Do | — |
| Krishi Sharma | KUNGFU.AI | Talk | Innovation in the Age of Regulation: Federated Learning with Flower | — |
| Laura Summers | Explosion | Talk | Ok, doomer | — |
| Lieke Kools | Sensorfact | Talk | Standby detection with a human in the loop | — |
| Maarten Oude Rikmanspoel | — | Talk | import full-focus as ff – How to reduce stress and pressure as a data specialist. | — |
| Maarten Sukel | Picnic | Talk | Multimodal Product Demand Forecasting: From pixels on your screen to a meal on your plate | — |
| Maël Deschamps | — | Talk | Our journey using data and AI to help monitor wildlife in parks in Africa | — |
| Mark Raasveldt | DuckDB Labs | Talk | In-Process Analytical Data Management with DuckDB | — |
| Maryam Miradi | Transactie Monitoring Nederland | Talk | Deep look into Deepfakes: Mastering Creation, Impact, and Detection | — |
| Nazli M. Alagoz | — | Talk | Staggered Difference-in-Differences in Practice: Causal Insights from the Music Industry | — |
| Niek IJzerman | City of Amsterdam | Talk | Using AI to make Amsterdam greener, safer and more accessible | — |
| Niklas Amberg | REWE Group | Talk | A data-driven approach for distributing scarce goods within the REWE retail supply chain | — |
| Okke van der Wal | Uber | Talk | Personalization at Uber scale via causal-driven machine learning | — |
| Oriol Abril Pla | Intuitive Bayes | Tutorial | Uncertainty visualization with ArviZ | — |
| Panos Alexopoulos | Textkernel | Tutorial | Mastering Knowledge Graph Modeling with Neo4j: A Practical Tutorial | — |
| Paul Zhutovsky | — | Talk | Transfer Learning in Boosting Models | — |
| Ramon Perez | Decoded | Tutorial | Unlocking the Black Box: A practical guide to finding an alibi for machine learning models | — |
| Raphael de Brito Tamaki | Meta | Talk | Forecasting Customer Lifetime Value (CLTV) for Marketing Campaigns under Uncertainty with PySTAN | — |
| Riccardo Amadio | Agile Lab | Talk | Declarative data manipulation pipeline with Dagster | — |
| Rik van der Vlist | Eneco | Talk | Balancing the electricity grid with multi-level forecasting models | — |
| Ritchie Vink | — | Talk | Polars and a peek in the expression engine | — |
| Robert Grimm | REWE Group | Talk | A data-driven approach for distributing scarce goods within the REWE retail supply chain | — |
| Roy van Santen | — | Tutorial | Designing a Machine Learning System | — |
| Shayla Jansen | City of Amsterdam | Talk | Using AI to make Amsterdam greener, safer and more accessible | — |
| Simone Gayed Said | — | Talk | Our journey using data and AI to help monitor wildlife in parks in Africa | — |
| sktime community | — | Talk | Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro | — |
| Till Döhmen | Hopsworks | Talk | MLOps on the fly: Optimizing a feature store with DuckDB and ArrowFlight | — |
| Tingting Qiao | — | Talk | Graph Neural Networks for Real World Fraud Detection | — |
| Tony Bagnall | University of East Anglia | Talk | Lets do the time warp again: time series machine learning with distance functions | — |
| Vincent Smeets | DPG Media | Talk | Revealing the True Motives of News Readers | — |
| Vincent Warmerdam | Explosion | Talk | There are no bad labels, only happy accidents | — |
| Wesley Boelrijk | Xccelerated | Talk | From Vision to Action: Designing and Deploying Effective Computer Vision Pipelines | — |
| Wessel Sandtke | Bookarang | Talk | Don’t judge a book by its cover: Using LLM created datasets to train models that detect literary features | — |
| Yorick van Pelt | Datakami | Tutorial | Building a personal search engine with llama-index | — |
| Ziad Al Moubayed | Adyen | Talk | Reliable and Scalable ML Serving: Best Practices for Online Model Deployment | — |
Sources
- https://web.archive.org/web/20230913034553/https://amsterdam.pydata.org/
- https://web.archive.org/web/20230913034540/https://amsterdam.pydata.org/tickets/
- https://web.archive.org/web/20240718043235/https://amsterdam2023.pydata.org/cfp/schedule/nojs
- https://web.archive.org/web/20240718051920/https://amsterdam2023.pydata.org/cfp/speaker/