Conferences / PyData
PyData Paris 2024
Sep 25–27, 2024 · Cité des Sciences et de l'Industrie, Centre des Congrès (Level -1), Paris, France
- Conference
- PyData Data science & ML
- Dates
- Sep 25, 2024 – Sep 27, 2024
- Venue
- Cité des Sciences et de l'Industrie, Centre des Congrès (Level -1)
- Place
- Paris, France
- Format
- In person
- Status
- Past
- Attendance
- —
- Website
- pydata.org
- Recorded
- 12 sponsors · 69 speaker appearances
- Notes
- Organized by QuantStack on behalf of NumFOCUS. Main conference 25-26 Sep 2024 (pretalx schedule runs to 27 Sep, incl. sprints). Ticket prices not found (Tito widget, no archived prices retrieved). Keynote affiliations from the conference homepage. Speakers: full list from the pretalx schedule export; affiliations parsed from pretalx speaker bios (null where the bio states no clear current employer/institution).
Ticket prices
No ticket prices recorded
Sponsors 12
Silver 2
Community 3
- Hub France IA · Community partners
- PyData Paris Meetup · Community partners
- Systematic Paris-Region · Community partners
Speakers and talks 69
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Katharine Jarmul | Thoughtworks | Keynote | Keynote: DIY Personalization: When, how and why to build your own models | — |
| Merve Noyan | Hugging Face | Keynote | Keynote: Open-source AI: why it matters and how to get started | — |
| Olivier Grisel | Probabl | Keynote | Keynote: Handling predictive uncertainty in Machine Learning | — |
| Sophia Yang | Mistral | Keynote | Keynote: Building with Mistral | — |
| Alexandre Abraham | Implicity | Talk | Dreadful Frailties in Propensity Score Matching and How to Fix Them. | — |
| Alexandre Carton | Renault | Talk | MLOps at Renault Group: A Generic Pipeline for Scalable Deployment | — |
| Alix Tiran-Cappello | Renault | Talk | MLOps at Renault Group: A Generic Pipeline for Scalable Deployment | — |
| Alonso Silva | Nokia | Talk | Enhancing RAG-based apps by constructing and leveraging knowledge graphs with open-weights LLMs | — |
| Alyssa Wright | — | Talk | Open Source Sustainability & Philanthropy: Building Contributor Communities | — |
| Andro Sabashvili | Bank of Georgia | Talk | Adaptive Prediction Intervals | — |
| Benedikt Heidrich | — | Talk | sktime - python toolbox for time series: next-generation AI – deep learning and foundation models | — |
| Benoît Courty | — | Talk | Track your code's C02 emissions with Code Carbon | — |
| Camilla Pacifici | Space Telescope Science Institute | Talk | Color-composite images from the James Webb Space Telescope | — |
| Cérès Carton | HelloWork | Talk | Leveraging LLMs to build supervised datasets suitable for smaller models | — |
| Cheuk Ting Ho | CMD Limes | Talk | Counting down for CRA - updates and expectations | — |
| Chiara Marmo | — | Talk | Jupylates: spaced repetition for teaching with Jupyter | — |
| Christophe Dervieux | Posit | Talk | Elevating Data Stories: Exploring Quarto Dashboard for impactful and visual communication | — |
| Comte Frédéric | — | Talk | Onyxia: A User-Centric Interface for Data Scientists in the Cloud Age | — |
| David Brochart | QuantStack | Talk | Collaborative editing in Jupyter | — |
| Devpriya Dave | Bloomberg | Talk | Open Source Sustainability & Philanthropy: Building Contributor Communities | — |
| Emanuele Fabbiani | EPFL | Talk | Is your marketing effective? Let Bayes decide! | — |
| Erik Welch | NVIDIA | Talk | Fast NetworkX and How Accelerated Backends Are Changing Graph Analytics | — |
| Francesc Martí Escofet | ETH Zurich | Talk | Catering Causal Inference: An Introduction to 'metalearners', a Flexible MetaLearner Library in Python | — |
| Franz Kiraly | — | Talk | sktime - python toolbox for time series: next-generation AI – deep learning and foundation models | — |
| Gabriele Orlandi | xtream | Talk | Foundational Models for Time Series Forecasting: are we there yet? | — |
| Guillaume Desforges | Modus Create | Talk | Processing medical images at scale on the cloud | — |
| Guillaume Lemaitre | — | Talk | An update on the latest scikit-learn features | — |
| Hendrik Makait | Coiled | Talk | Geoscience at Massive Scale | — |
| Ian Thomas | QuantStack | Talk | JupyterLite, Emscripten-forge, Xeus, and Mamba -- The computational quartet for in browser interactive computing" | — |
| Iisakki Rotko | Widgetti | Talk | Solara: Pure Python web apps beyond prototypes and dashboards | Slides |
| Inácio Medeiros | Institut Curie | Talk | Unveiling new maps of biology with Squidpy | — |
| Jeremy Tuloup | — | Talk | JupyterLite, Emscripten-forge, Xeus, and Mamba -- The computational quartet for in browser interactive computing" | — |
| Jesse Averbukh | — | Talk | Color-composite images from the James Webb Space Telescope | — |
| Johan Mabille | — | Talk | Unveiling Mamba 2.0: The Future of Fast Package Management | — |
| Johannes Bechberger | SAP | Talk | Python 3.12's new monitoring and debugging API | — |
| Joris Van den Bossche | Voltron Data | Talk | The expanding Apache Arrow universe - standardizing and accelerating tabular data access and interchange | — |
| Joseph Garrone | — | Talk | Onyxia: A User-Centric Interface for Data Scientists in the Cloud Age | — |
| Julien Jerphanion | QuantStack | Talk | Unveiling Mamba 2.0: The Future of Fast Package Management | — |
| Justine BEL-LETOILE | HelloWork | Talk | Leveraging LLMs to build supervised datasets suitable for smaller models | — |
| Kevin Klein | QuantCo | Talk | Catering Causal Inference: An Introduction to 'metalearners', a Flexible MetaLearner Library in Python | — |
| Lino Galiana | INSEE | Talk | Onyxia: A User-Centric Interface for Data Scientists in the Cloud Age | — |
| Louis Lacombe | Capgemini | Talk | Boosting AI Reliability: Uncertainty Quantification with MAPIE | — |
| Luca Baggi | xtream | Talk | Foundational Models for Time Series Forecasting: are we there yet? | — |
| Luis Blanche | — | Talk | Track your code's C02 emissions with Code Carbon | — |
| Maarten Breddels | Widgetti | Talk | Solara: Pure Python web apps beyond prototypes and dashboards | Slides |
| Manon Marchand | Strasbourg Astronomical Data Centre | Talk | Visualization of the sky in Notebooks: the ipyaladin widget extension | — |
| Marco Gorelli | Quansight | Talk | Polars Plugins: how you (yes, you!) can extend Polars | — |
| Maria Knorps | — | Talk | On the structure and reproducibility of Python packages - data crunch | — |
| Maria Knorps | — | Talk | Evaluating the evaluator: RAG eval libraries under the loop | — |
| Matthieu Baumann | Strasbourg | Talk | Visualization of the sky in Notebooks: the ipyaladin widget extension | — |
| Max Halford | — | Talk | Unpack business metrics to explain their evolution | — |
| Nichita Morcotilo | — | Talk | Bridging the worlds: pixi reimplements pip and conda in Rust | — |
| Nicolas Guenon des Mesnards | GitGuardian | Talk | Would you rely on ChatGPT to dial 911? A talk on balancing determinism and probabilism in production machine learning systems | — |
| Nicolas M. Thiéry | Laboratoire Interdisciplinaire | Talk | Jupylates: spaced repetition for teaching with Jupyter | — |
| Noé Achache | Sicara | Talk | Towards a deeper understanding of retrieval and vector databases | — |
| Nour El Mawass | Modus Create | Talk | Evaluating the evaluator: RAG eval libraries under the loop | — |
| Patrick Hoefler | Coiled | Talk | Building Large Scale ETL Pipelines with Dask | — |
| Pierre Raybaut | — | Talk | DataLab: Bridging Scientific and Industrial Worlds for Advanced Signal and Image Processing | — |
| Quentin Gaudel | Mercator Ocean | Talk | Onyxia: A User-Centric Interface for Data Scientists in the Cloud Age | — |
| Rick Ratzel | NVIDIA | Talk | Fast NetworkX and How Accelerated Backends Are Changing Graph Analytics | — |
| Serge « sans » Paille | — | Talk | xsimd: from xtensor to firefox | — |
| Simeon Carstens | Modus Create | Talk | Chainsail: facilitating sampling of multimodal probability distributions | — |
| Stefanie Senger | — | Talk | An update on the latest scikit-learn features | — |
| Thibault Cordier | Capgemini | Talk | Boosting AI Reliability: Uncertainty Quantification with MAPIE | — |
| Thorsten Beier | QuantStack | Talk | JupyterLite, Emscripten-forge, Xeus, and Mamba -- The computational quartet for in browser interactive computing" | — |
| Tim Paine | Cubist Systematic Strategies | Talk | High Performance Data Visualization for the Web | — |
| Trung Le | QuantStack | Talk | Building web-based engineering applications with JupyterLab components. | — |
| Valentin Laurent | Capgemini | Talk | Boosting AI Reliability: Uncertainty Quantification with MAPIE | — |
| Zhihan Zhang | — | Talk | On the structure and reproducibility of Python packages - data crunch | — |