{"$schema": "https://c3voc.de/schedule/schema.json", "generator": {"name": "pretalx", "version": "2026.3.0.dev0", "url": "https://pretalx.com"}, "schedule": {"url": "https://pretalx.com/pydata-paris-2024/schedule/", "version": "0.9", "base_url": "https://pretalx.com", "conference": {"acronym": "pydata-paris-2024", "title": "PyData Paris 2024", "start": "2024-09-25", "end": "2024-09-27", "daysCount": 3, "timeslot_duration": "00:05", "time_zone_name": "Europe/Paris", "colors": {"primary": "#383E41"}, "rooms": [{"name": "Carrefour Num\u00e9rique", "slug": "3508-carrefour-numerique", "guid": "dda6f6f9-f47c-5897-bf13-95e4959c75d4", "description": null, "capacity": 150}, {"name": "Gaston Berger", "slug": "3190-gaston-berger", "guid": "2270a2e4-3e67-5ddd-9a69-654508db4d0b", "description": null, "capacity": 1000}, {"name": "Louis Armand 2 - Ouest", "slug": "3192-louis-armand-2-ouest", "guid": "c7fe3e2c-fa4b-5632-bd1d-1bff340d2531", "description": null, "capacity": 200}, {"name": "Louis Armand 1 - Est", "slug": "3191-louis-armand-1-est", "guid": "d1698132-d5cb-5c6f-a0c5-2d48f9582a8f", "description": null, "capacity": 200}], "tracks": [], "days": [{"index": 1, "date": "2024-09-25", "day_start": "2024-09-25T04:00:00+02:00", "day_end": "2024-09-26T03:59:00+02:00", "rooms": {"Gaston Berger": [{"guid": "a86b0225-c952-5453-bf38-7ead2a2d4807", "code": "RDWBWQ", "id": 50117, "logo": null, "date": "2024-09-25T09:00:00+02:00", "start": "09:00", "end": "2024-09-25T09:45:00+02:00", "duration": "00:45", "room": "Gaston Berger", "slug": "pydata-paris-2024-50117-keynote-diy-personalization-when-how-and-why-to-build-your-own-models", "url": "https://pretalx.com/pydata-paris-2024/talk/RDWBWQ/", "title": "Keynote: DIY Personalization: When, how and why to build your own models", "subtitle": "", "track": null, "type": "Keynote", "language": "en", "abstract": "With increased ease of smaller \"AI\" models, better chips and on-device learning, is it possible now to build and train your own models for your own use? In this keynote, we'll explore learnings of small, medium and large-sized model personalization, but driven by yourself and for yourself. A walk through what's possible, what's not and what we should prioritize if we'd like AI & ML to be made for everyone.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "K9B9W9", "name": "Katharine Jarmul", "avatar": "https://pretalx.com/media/avatars/K9B9W9_MH2To00.webp", "biography": "Katharine Jarmul is a privacy activist and author of Practical Data Privacy (O'Reilly 2023). She has worked in privacy, security & ethics in machine learning & data for more than 10 years and rants about building things differently at probablyprivate.com.", "public_name": "Katharine Jarmul", "guid": "61e0d092-18c4-594d-9aa5-bb4e3935f3f7", "url": "https://pretalx.com/pydata-paris-2024/speaker/K9B9W9/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/RDWBWQ/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/RDWBWQ/", "attachments": []}, {"guid": "cfb7ee6f-633e-5931-8927-9f6849d22ee4", "code": "QQRKPG", "id": 48476, "logo": null, "date": "2024-09-25T09:45:00+02:00", "start": "09:45", "end": "2024-09-25T10:15:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-48476-building-web-based-engineering-applications-with-jupyterlab-components", "url": "https://pretalx.com/pydata-paris-2024/talk/QQRKPG/", "title": "Building web-based engineering applications with JupyterLab components.", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "In the past few years, web-based engineering software has been steadily gaining momentum over traditional desktop-based applications. It represents a significant shift in how engineers access, collaborate, and utilize software tools for design, analysis, and simulation tasks. However, converting desktop-based applications to web applications presents considerable challenges, especially in translating the functionality of desktop interfaces to the web. It requires careful planning and design expertise to ensure intuitive navigation and responsiveness.\n\nJupyterLab provides a flexible, interactive environment for scientific computing. Despite its popularity among data scientists and researchers, the full potential of JupyterLab as a platform for building scientific web applications has yet to be realized.\n\nIn this talk, we will explore how its modular architecture and extensive ecosystem facilitate the seamless integration of components for diverse functionalities: from rich user interfaces, accessibility, and real-time collaboration to cloud deployment options. To illustrate the platform's capabilities, we will demo JupyterCAD, a parametric 3D modeler built on top of JupyterLab components.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "7UY3GR", "name": "Trung Le", "avatar": "https://pretalx.com/media/avatars/7UY3GR_fAdtAAh.webp", "biography": "Le Duc Trung is a Scientific Software Developer at QuantStack. He works on several projects within the Jupyter ecosystem, from the main projects like JupyterLab, Voil\u00e0, and ipywidgets to various JupyterLab extensions and widgets.", "public_name": "Trung Le", "guid": "78619136-ac46-59b0-8bf5-329073e0654c", "url": "https://pretalx.com/pydata-paris-2024/speaker/7UY3GR/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/QQRKPG/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/QQRKPG/", "attachments": []}, {"guid": "0e1bffe7-501f-559a-aae1-9fc9212eef5c", "code": "HMMGLA", "id": 48466, "logo": null, "date": "2024-09-25T10:30:00+02:00", "start": "10:30", "end": "2024-09-25T11:00:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-48466-collaborative-editing-in-jupyter", "url": "https://pretalx.com/pydata-paris-2024/talk/HMMGLA/", "title": "Collaborative editing in Jupyter", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "The Jupyter stack has undergone a significant transformation in recent years with the integration of collaborative editing features: users can now modify a shared document and see each other's changes in real time, with a user experience akin to that of Google Docs. The underlying technology uses a special data structure called Conflict-free Replicated Data Types (CRDTs), that automatically resolves conflicts when concurrent changes are made. This allows data to be distributed rather than centralized in a server, letting clients work as if data was local rather than remote.\nIn this talk, we look at new possibilities that CRDTs can unlock, and how they are redefining Jupyter's architecture. Different use cases are presented: a suggestion system similar to Google Doc's, a chat system allowing collaboration with an AI agent, an execution model allowing full notebook state recovery, a collaborative widget model. We also look at the benefits of using CRDTs in JupyterLite, where users can interact without a server. This may be a great example of a distributed system where every user owns their data and shares them with their peers.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "LVS7K9", "name": "David Brochart", "avatar": "https://pretalx.com/media/avatars/LVS7K9_ZxyaOcr.webp", "biography": "David Brochart is a Technical Director at QuantStack. David is a contributor to the Jupyter ecosystem. He focuses on the backend, mostly developing collaborative editing. He is also the main author of Jupyverse, a next-generation Jupyter server.", "public_name": "David Brochart", "guid": "27b30dd7-3ae7-5097-aef8-e98b21cef604", "url": "https://pretalx.com/pydata-paris-2024/speaker/LVS7K9/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/HMMGLA/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/HMMGLA/", "attachments": []}, {"guid": "a0019821-224c-5c7b-83c1-1ae8077bd968", "code": "JAMSS3", "id": 48506, "logo": null, "date": "2024-09-25T11:05:00+02:00", "start": "11:05", "end": "2024-09-25T11:35:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-48506-jupylates-spaced-repetition-for-teaching-with-jupyter", "url": "https://pretalx.com/pydata-paris-2024/talk/JAMSS3/", "title": "Jupylates: spaced repetition for teaching with Jupyter", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Jupyter based environments are getting a lot of traction for teaching computing, programming, and data sciences. The narrative structure of notebooks has indeed proven its value for guiding each student at it's own pace to the discovery and understanding of new concepts or new idioms (e.g. how do I extract a column in pandas?). But then these new pieces of knowledge tend to quickly fade out and be forgotten. Indeed long term acquisition of knowledge and skills takes reinforcement by repetition. This is the foundation of many online learning platforms like [Webwork](https://en.wikipedia.org/wiki/WeBWorK) or [WIMS](https://fr.wikipedia.org/wiki/WIMS) that offer exercises with randomization and automatic feedback. And of popular \"AI-powered\" apps -- e.g. to learn foreign languages -- that use spaced repetition algorithms designed by educational and neuro sciences to deliver just the right amount of repetition.\n\nWhat if you could author such exercizes as notebooks, to benefit from everything that Jupyter can offer (think rich narratives, computations, visualization, interactions)? What if you could integrate such exercises right into your Jupyter based course? What if a learner could get personalized exercise recommandations based on their past learning records, without having to give away these sensitive pieces of information away?\n\nThat's [Jupylates](https://gitlab.dsi.universite-paris-saclay.fr/jupyter/jupylates/) (work in progress). And thanks to the open source scientific stack, it's just a small Jupyter extension.", "description": "In this talk, we will first briefly review our [usage of Jupyter for teaching at Universit\u00e9 Paris-Saclay](https://jupyter.gitlab.dsi.universite-paris-saclay.fr/README.html), to motivate how we came to develop Jupylates. Then, after a quick demo, we will discuss its design, describe its current and upcoming features, and report on its usage in two large freshmen classes in 2023-2024.\n\nTime permitting, we will zoom out and deliver a brief tour of our Jupyter course infrastructure, advertising in particular [Travo](https://gitlab.com/travo-cr/travo/) for GitLab-based nbgrader-compatible assignment management.", "recording_license": "", "do_not_record": false, "persons": [{"code": "K7CTQY", "name": "Chiara Marmo", "avatar": null, "biography": null, "public_name": "Chiara Marmo", "guid": "106d8fa4-dd15-5cc1-b0c8-a85db1e1871e", "url": "https://pretalx.com/pydata-paris-2024/speaker/K7CTQY/"}, {"code": "7JHTUX", "name": "Nicolas M. Thi\u00e9ry", "avatar": "https://pretalx.com/media/avatars/7JHTUX_vJKw8Ox.webp", "biography": "Professor of Computer Science at the [Laboratoire Interdisciplinaire des Sciences du Num\u00e9riques](https://www.lisn.upsaclay.fr/) of [Universit\u00e9 Paris-Saclay](https://www.universite-paris-saclay.fr/). [Read more](https://nicolas.thiery.name/).", "public_name": "Nicolas M. Thi\u00e9ry", "guid": "03038c76-98d4-5ffb-ac81-8aa3d00fe1c8", "url": "https://pretalx.com/pydata-paris-2024/speaker/7JHTUX/"}], "links": [{"title": "Screenshot", "url": "https://gitlab.dsi.universite-paris-saclay.fr/jupyter/jupylates/-/raw/master/screenshot.png?ref_type=heads", "type": "related"}], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/JAMSS3/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/JAMSS3/", "attachments": []}, {"guid": "cee2aebe-2f7d-5310-a0c7-accc3424250c", "code": "LVSPQG", "id": 48583, "logo": null, "date": "2024-09-25T11:40:00+02:00", "start": "11:40", "end": "2024-09-25T12:10:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-48583-jupyterlite-emscripten-forge-xeus-and-mamba-the-computational-quartet-for-in-browser-interactive-computing", "url": "https://pretalx.com/pydata-paris-2024/talk/LVSPQG/", "title": "JupyterLite, Emscripten-forge, Xeus, and Mamba --  The computational quartet for in browser interactive computing\"", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "JupyterLite is a JupyterLab distribution that runs entirely in the web browser, backed by in-browser language kernels. With standard JupyterLab, where kernels run in separate processes and communicate with the client by message passing, JupyterLite uses kernels that run entirely in the browser, based on JavaScript and WebAssembly. \n\nThis means JupyterLite deployments can be scaled to millions of users without the need for individual containers for each user session, only static files need to be served, which can be done with a simple web server like GitHub pages.\n\nThis opens up new possibilities for large-scale deployments, eliminating the need for complex cloud computing infrastructure. JupyterLite is versatile and supports a wide range of languages, with the majority of its kernels implemented using Xeus, a C++ library for developing language-specific kernels.\n\nIn conjunction with JupyterLite, we present Emscripten-forge, a conda/mamba based distribution for WebAssembly packages. Conda-forge is a community effort and a GitHub organization which contains repositories of conda recipes and thus provides conda packages for a wide range of software and platforms. However, targeting WebAssembly is not supported by conda-forge. Emscripten-forge addresses this gap by providing conda packages for WebAssembly, making it possible to create custom JupyterLite deployments with tailored conda environments containing the required kernels and packages.\n\nIn this talk, we delve deep into the JupyterLite ecosystem, exploring its integration with Xeus Mamba  and Emscripten-forge. \n\nWe will demonstrate how this can be used to create sophisticated JupyterLite deployments with custom conda environments and give an outlook for future developments like R packages and runtime package resolution.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "MAZXW8", "name": "Jeremy Tuloup", "avatar": "https://pretalx.com/media/avatars/MAZXW8_w5NxSev.webp", "biography": null, "public_name": "Jeremy Tuloup", "guid": "c841caff-f055-5a3b-bfe0-2b195168d740", "url": "https://pretalx.com/pydata-paris-2024/speaker/MAZXW8/"}, {"code": "TDTZCJ", "name": "Thorsten Beier", "avatar": null, "biography": "Thorsten Beier is a Scientific Software Developer at QuantStack. He got his PhD at the University of Heidelberg (Germany) on Multicut Algorithms for Neurite Segmentation.", "public_name": "Thorsten Beier", "guid": "05eba633-14b8-57ce-adec-5cbc647c682f", "url": "https://pretalx.com/pydata-paris-2024/speaker/TDTZCJ/"}, {"code": "NKXZDM", "name": "Ian Thomas", "avatar": "https://pretalx.com/media/avatars/NKXZDM_fwMiqix.webp", "biography": "Ian is a Scientific Software Developer at QuantStack working in the Jupyter ecosystem on JupyterLite and kernels. He is also a core maintainer of Matplotlib, Bokeh and ContourPy. Ian is British and drinks lots of tea.", "public_name": "Ian Thomas", "guid": "8c933412-76e8-5891-baa0-ecca1de4dff0", "url": "https://pretalx.com/pydata-paris-2024/speaker/NKXZDM/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/LVSPQG/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/LVSPQG/", "attachments": []}, {"guid": "b340b93e-32f6-55df-9389-2e2a0c4a3ab2", "code": "JVGAEU", "id": 48671, "logo": null, "date": "2024-09-25T13:50:00+02:00", "start": "13:50", "end": "2024-09-25T14:20:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-48671-building-large-scale-etl-pipelines-with-dask", "url": "https://pretalx.com/pydata-paris-2024/talk/JVGAEU/", "title": "Building Large Scale ETL Pipelines with Dask", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Building scalable ETL pipelines and deploying them in the cloud can seem daunting. It shouldn't be. Leveraging proper technologies can make this process easy. We will discuss the whole process of developing a composable and scalable ETL pipeline centred around Dask that is fully built with Open Source tools and how we can deploy to the cloud.", "description": "Running regular large-scale Python jobs on the cloud as part of production data pipelines is a common requirement. Building scalable ETL pipelines operating at scale and deploying them in the cloud can seem daunting. Managing different tools, building an infrastructure that can scale to large datasets and making sure that things run reliable has many complexities.\n\nModern workflow orchestration systems like Prefect, Dagster, Airflow, etc. all work well for running jobs on a regular cadence. Dask fulfils the scalability requirement and storage formats like Delta Lake or Iceberg can both deal with large scale data. \n\nDask is a library for distributed computing with Python and integrates tightly with pandas and other libraries from the PyData stack. It offers a DataFrame API that wraps pandas and thus offers an easy transition into the big data space. We will dive into recent Dask developments like a new shuffle algorithm, a query optimizer and other smaller changes and investigate how they improve scalability and performance to make Dask a great fit for large scale ETL pipelines. Gone are the days when Dask performed poorly on benchmarks, the new Dask DataFrame implementation can now beat Spark on the popular TPC-H benchmarks in cost and performance. \n\nWe will illustrate how all those tools fit very nicely and how we can glue them together to create a composable ETL pipeline. This includes the infrastructure and architecture of the pipeline. We will look at a live example how our pipeline operates. We discuss how we can test locally and seamlessly switch deployment to the cloud.", "recording_license": "", "do_not_record": false, "persons": [{"code": "STZ9DS", "name": "Patrick Hoefler", "avatar": "https://pretalx.com/media/avatars/STZ9DS_Jlgi0Pr.webp", "biography": "Patrick Hoefler is a member of the pandas core team and a Dask maintainer. He is currently working at Coiled where he focuses on Dask development and the integration of a logical query planning layer into Dask. He holds a Msc degree in Mathematics and works towards a Msc in Software engineering at the University of Oxford.", "public_name": "Patrick Hoefler", "guid": "a28da85d-aef6-5a6b-a43b-0b482ea06e44", "url": "https://pretalx.com/pydata-paris-2024/speaker/STZ9DS/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/JVGAEU/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/JVGAEU/", "attachments": []}, {"guid": "04753e6e-e28a-51ea-a995-b2fc05d0b9e5", "code": "NNWFC3", "id": 48539, "logo": null, "date": "2024-09-25T14:25:00+02:00", "start": "14:25", "end": "2024-09-25T14:55:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-48539-the-expanding-apache-arrow-universe-standardizing-and-accelerating-tabular-data-access-and-interchange", "url": "https://pretalx.com/pydata-paris-2024/talk/NNWFC3/", "title": "The expanding Apache Arrow universe - standardizing and accelerating tabular data access and interchange", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Apache Arrow has become a de-facto standard for efficient in-memory columnar data representation. Beyond the standardized and language-independent columnar memory format for tabular data, the Apache Arrow project also has a growing set of supplementary specifications and language implementations. This talk will give an overview of the recent developments in the Apache Arrow ecosystem, including ADBC, nanoarrow, new data types, and the Arrow PyCapsule protocol.", "description": "According to the website, Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing. At its core is the Arrow Columnar Format, a language-independent columnar memory format for tabular data. But the Arrow project encompasses many things, including serialization, messaging and database specifications and a variety of language implementations. And this ecosystem of specifications and tools (and libraries adopting those) is constantly growing.\n\nAfter a brief recap of the Apache Arrow project to set the stage, this talk will give an overview of the recent developments in the Apache Arrow ecosystem: from additions to the core Columnar Format (such as the string view type), newer specifications (such as the ADBC database specification, additions to Flight SQL, and the Arrow PyCapsule protocol) and implementations (such as nanoarrow).", "recording_license": "", "do_not_record": false, "persons": [{"code": "7VUXWM", "name": "Joris Van den Bossche", "avatar": "https://pretalx.com/media/avatars/7VUXWM_nRBADDN.webp", "biography": "I am a core contributor to pandas and Apache Arrow, and a maintainer of GeoPandas. I did a PhD at Ghent University and VITO in air quality research and worked at the Paris-Saclay Center for Data Science. Currently, I work at Voltron Data, contributing to Apache Arrow, and am a freelance teacher of python (pandas) at Ghent University.", "public_name": "Joris Van den Bossche", "guid": "1f18915a-a2f7-51e7-96cf-82152ec96930", "url": "https://pretalx.com/pydata-paris-2024/speaker/7VUXWM/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/NNWFC3/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/NNWFC3/", "attachments": []}, {"guid": "fb555053-62b9-552f-9419-50ee9f014f00", "code": "HHNHMJ", "id": 46772, "logo": "https://pretalx.com/media/pydata-paris-2024/submissions/HHNHMJ/polar_plugin_small_iKtMvsX.jpg", "date": "2024-09-25T15:00:00+02:00", "start": "15:00", "end": "2024-09-25T15:30:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-46772-polars-plugins-how-you-yes-you-can-extend-polars", "url": "https://pretalx.com/pydata-paris-2024/talk/HHNHMJ/", "title": "Polars Plugins: how you (yes, you!) can extend Polars", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Polars is a dataframe library taking the world by storm. It is very runtime and memory efficient and comes with a clean and expressive API. Sometimes, however, the built-in API isn't enough. And that's where its killer feature comes in: plugins. You can extend Polars, and solve practically any problem.\n\nNo prior Rust experience required, intermediate Python and programming experience required. By the end of the talk, you will know how to write your own Polars Plugin! This talk is aimed at data practitioners.", "description": "Have you ever had the experience of needing to write a really custom function? Did you end up using a custom Python lambda function and waiting endlessly whilst your code executed?\n\nLet's learn how to put an end to that!\n\nThis talk is aimed at advanced dataframe users who want to go beyond what Polars offers them. The structure will be:\n- 5 minutes motivation: example of a custom function which is painfully slow\n- 5 minutes prerequisites: what's Polars Series? What's a Chunked Array? What's an Arrow Array? What's a validity mask?\n- 5 minutes Rust basics: the bare minimum, and recommended resources to learn it properly\n- 5 minutes: how to translate Python lambda functions to Rust plugins: elementwise single-column example (take the absolute value of each element), then non-elementwise example (cumulative sum)\n- 5 minutes addressing the motivating example with a Polars plugin, show how much faster it became with only ~30 lines of Rust code\n- 5 minutes: Q&A\n\nBy the end of the session, attendees will know how to write their own Polars Plugin, and how to go about learning any prerequisites they may currently lack (such as Rust). This talk is aimed at data practitioners who have experience with Python and data analysis (however, no prior Rust experience is required!).", "recording_license": "", "do_not_record": false, "persons": [{"code": "KEUJ9U", "name": "Marco Gorelli", "avatar": "https://pretalx.com/media/avatars/KEUJ9U_Q9qz0Ku.webp", "biography": "Marco is a core dev of pandas and Polars and works at Quansight Labs as Senior Software Engineer. He also consults and trains clients professionally on Polars. He has also written the first Polars Plugins Tutorial and has taught Polars Plugins to clients.\n\nHe has a background in Mathematics and holds an MSc from the University of Oxford, and was one of the prize winners in the M6 Forecasting Competition (2nd place overall Q1).", "public_name": "Marco Gorelli", "guid": "2d6c7d56-9236-5478-9821-f6b849f264bd", "url": "https://pretalx.com/pydata-paris-2024/speaker/KEUJ9U/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/HHNHMJ/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/HHNHMJ/", "attachments": []}, {"guid": "a3e0876a-7f1c-5b0b-96e9-0ad2a4f57c6c", "code": "J8GMY3", "id": 51836, "logo": null, "date": "2024-09-25T15:45:00+02:00", "start": "15:45", "end": "2024-09-25T16:30:00+02:00", "duration": "00:45", "room": "Gaston Berger", "slug": "pydata-paris-2024-51836-keynote-open-source-ai-why-it-matters-and-how-to-get-started", "url": "https://pretalx.com/pydata-paris-2024/talk/J8GMY3/", "title": "Keynote: Open-source AI: why it matters and how to get started", "subtitle": "", "track": null, "type": "Keynote", "language": "en", "abstract": "In this talk, we will go through everything open-source AI: the state of open-source AI, why it matters, the future of it and how you can get started with it.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "QKYJWS", "name": "Merve Noyan", "avatar": "https://pretalx.com/media/avatars/QKYJWS_7WST2Ue.webp", "biography": "Merve is a machine learning advocate engineer at Hugging Face. She enjoys contributing to open-source and researching about multimodality, foundation vision models and how to make them more accessible through optimization.", "public_name": "Merve Noyan", "guid": "eafcd844-3722-5aae-9891-480cb3ae44d0", "url": "https://pretalx.com/pydata-paris-2024/speaker/QKYJWS/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/J8GMY3/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/J8GMY3/", "attachments": []}], "Louis Armand 1 - Est": [{"guid": "2b95e245-eebd-5033-a385-fd237d6aef0c", "code": "GT83UH", "id": 48573, "logo": null, "date": "2024-09-25T09:45:00+02:00", "start": "09:45", "end": "2024-09-25T10:15:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48573-would-you-rely-on-chatgpt-to-dial-911-a-talk-on-balancing-determinism-and-probabilism-in-production-machine-learning-systems", "url": "https://pretalx.com/pydata-paris-2024/talk/GT83UH/", "title": "Would you rely on ChatGPT to dial 911? A talk on balancing determinism and probabilism in production machine learning systems", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "In the last year there hasn\u2019t been a day that passed without us hearing about a new generative AI innovation that will enhance some aspect of our lives. On a number of tasks large probabilistic systems are now outperforming humans, or at least they do so \u201con average\u201d. \u201cOn average\u201d means most of the time, but in many real life scenarios \u201caverage\u201d performance is not enough: we need correctness ALL of the time, for example when you ask the system to dial 911. \n\nIn this talk we will explore the synergy between deterministic and probabilistic models to enhance the robustness and controllability of machine learning systems. Tailored for ML engineers, data scientists, and researchers, the presentation delves into the necessity of using both deterministic algorithms and probabilistic model types across various ML systems, from straightforward classification to advanced Generative AI models. \n\nYou will learn about the unique advantages each paradigm offers and gain insights into how to most effectively combine them for optimal performance in real-world applications. I will walk you through my past and current experiences in working with simple and complex NLP models, and show you what kind of pitfalls, shortcuts, and tricks are possible to deliver models that are both competent and reliable.\n\nThe session will be structured into a brief introduction to both model types, followed by case studies in classification and generative AI, concluding with a Q&A segment.", "description": "Objective and Outline:\nThis talk addresses the often-overlooked need for integrating deterministic and probabilistic models in machine learning, which is crucial in complex production environments. We begin by defining deterministic and probabilistic models, highlighting their distinct roles in ML systems. The talk then showcases practical examples where the synergy of these models enhances system performance, focusing on classification and Generative AI models.\n\nTarget Audience and Expected Background Knowledge:\nIntended for ML engineers, data scientists, and academic researchers, this presentation assumes familiarity with basic machine learning concepts and models. It's particularly beneficial for those involved in designing, implementing, or managing ML systems in production environments.\n\nKey Takeaways:\n\n- Understanding the strengths and limitations of deterministic and probabilistic models in ML.\n- Strategies for effectively combining these models in various ML systems.\n- Real-world examples demonstrating the improved robustness and controllability achieved through this integration.\n- Insights into future trends and potential developments in model integration.\n\nTime Breakdown:\n\n- Minutes 0-10: Introduction to deterministic and probabilistic models\n- Minutes 10-20: Synergies of approaches in real-world examples\n- Minutes 20-30: Applications for Generative AI models, including Q&A\n\nAdditional Information:\nNo prerequisites are required beyond a basic understanding of machine learning concepts. The presentation will be informative with a focus on practical applications, providing attendees with actionable knowledge and a deeper appreciation of model integration in ML systems.", "recording_license": "", "do_not_record": false, "persons": [{"code": "XHFQVH", "name": "Nicolas Guenon des Mesnards", "avatar": "https://pretalx.com/media/avatars/XHFQVH_8JlYrCC.webp", "biography": "Nicolas is a Sr. ML Engineer at GitGuardian where he develops NLP-based technologies to detect vulnerabilities in code and provide remediation. He was previously Sr. Applied Scientist at Amazon Alexa  where he developed the models that power Alexa's core understanding  capabilities. He published multiple academic papers at top tier NLP conferences in the field of semantic parsing. Nicolas has hands-on experience with a variety of NLP models applied to client-facing applications.", "public_name": "Nicolas Guenon des Mesnards", "guid": "9ee0e513-2e15-5009-9d61-bce55744284d", "url": "https://pretalx.com/pydata-paris-2024/speaker/XHFQVH/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/GT83UH/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/GT83UH/", "attachments": [{"title": "Slides of presentation", "url": "/media/pydata-paris-2024/submissions/GT83UH/resources/PydataParis2024_NicolasGuenonDesMesnards__PxZHDZx.pdf", "type": "related"}]}, {"guid": "3889f12e-a965-591c-b991-c198093eb65f", "code": "KLBGJA", "id": 48503, "logo": null, "date": "2024-09-25T10:30:00+02:00", "start": "10:30", "end": "2024-09-25T11:00:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48503-evaluating-the-evaluator-rag-eval-libraries-under-the-loop", "url": "https://pretalx.com/pydata-paris-2024/talk/KLBGJA/", "title": "Evaluating the evaluator: RAG eval libraries under the loop", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Retrieval-augmented generation (RAG) has become a key application for large language models (LLMs), enhancing their responses with information from external databases. However, RAG systems are prone to errors, and their complexity has made evaluation a critical and challenging area. Various libraries (like RAGAS and TruLens) have introduced evaluation tools and metrics for RAGs, but these evaluations involve using one LLM to assess another, raising questions about their reliability. Our study examines the stability and usefulness of these evaluation methods across different datasets and domains, focusing on the effects of the choice of the evaluation LLM, query reformulation, and dataset characteristics on RAG performance. It also assesses the stability of the metrics on multiple runs of the evaluation and how metrics correlate with each other. The talk aims to guide users in selecting and interpreting LLM-based evaluations effectively.", "description": "Retrieval-augmented generation (RAG) has emerged as a common technique to augment LLMs. It provides more founded answers based on relevant content from databases and documents, thus offering a cheap and convenient alternative to fine-tuning and re-training.\n\nHowever, a system that uses RAG can and will make mistakes, and the space of parameters one can tune in a RAG system is increasingly more complex. Evaluation has therefore understandably become a hot topic in the community. Many actors (e.g. RAGAS, TruLens, MLFlow) have developed libraries and RAG-specific metrics that allow for a faster, more principled and more easily scalable evaluation.\nThis comes with a catch though: in a way, you're asking an LLM (albeit an admittedly high-grade one) to evaluate another LLM.\n\nTo understand the usefulness and stability of RAG evaluation libraries, we have conducted an analysis of RAG evaluation results on two different datasets of corporate and technical documentation, representing different levels of technical difficulty.\n\nManual evaluation is a time consuming process and we only provide a limited comparison of LLM-based results to manual ones. Instead, we dedicate the bulk of the analysis to more systematic ways of measuring how stable the results are. We study for instance the impact of the evaluator LLM choice and query reformulation on retrieval and generation in RAG, and re-run LLMs on both sides (RAG and evaluation) multiple times to assess the bounds of the metrics. We also study the correlation between different evaluation metrics and the impact of the dataset, its domain and its size.\n\nThe goal of this talk is to provide the audience with the insights, caveats and better intuition they need when choosing, using and interpreting the results of LLM-based evaluation. \n\nSome prior knowledge in RAG is useful, but not required.", "recording_license": "", "do_not_record": false, "persons": [{"code": "3U78WE", "name": "Nour El Mawass", "avatar": "https://pretalx.com/media/avatars/3U78WE_Kpzhqxb.webp", "biography": "Nour leads the Generative AI technical group at Modus Create. She has a PhD in Machine Learning. and has worked on Machine Learning, Data Science and Data Engineering problems in various domains, both inside and outside Academia.", "public_name": "Nour El Mawass", "guid": "261c24aa-a285-5bd3-bb90-27426631e072", "url": "https://pretalx.com/pydata-paris-2024/speaker/3U78WE/"}, {"code": "DNCKCS", "name": "Maria Knorps", "avatar": "https://pretalx.com/media/avatars/DNCKCS_WyTl55M.webp", "biography": "Maria's professional goal is to improve the environment by understanding it first in\n  the language of mathematics and then applying the gained knowledge.\n  After graduating in applied mathematics, Maria began research on the two-phase turbulent flows.\n  Knowledge of mathematical modeling helped her better understand the small-scale physical effects \n  and allowed her to more accurately model two-phase turbulence while reducing computational costs.\n\n  After completing her PhD, Maria began to work as a data scientist. \n  She was responsible for all stages of data processing, from creating ETL pipelines, through modeling \n  to visualization of the results and leading 2-5 people projects.\n  Her inclination towards the implementation and design aspects gravitated her towards functional programming.\n  She integrated Haskell into parts of the data processing pipelines, finding its type system and\n  expressiveness more akin to mathematical language. Maria is also dedicated to maintaining neat,\n  reusable, and well-documented code.\n\n  Outside of her technical pursuits, Maria is passionate about promoting diversity in the IT industry\n  and inspiring girls and women to engage in programming. Balancing her career with being a mother of three,\n  she finds limited but cherished time for personal hobbies. When the opportunity arises,\n  Maria enjoys the thrill of motorcycle rides beyond the city limits.", "public_name": "Maria Knorps", "guid": "731efbea-53d3-5dc5-b5e3-7d9075a3491a", "url": "https://pretalx.com/pydata-paris-2024/speaker/DNCKCS/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/KLBGJA/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/KLBGJA/", "attachments": []}, {"guid": "1d7f639d-60af-5d50-b229-adb4614b8006", "code": "AZNEHE", "id": 48288, "logo": null, "date": "2024-09-25T11:05:00+02:00", "start": "11:05", "end": "2024-09-25T11:35:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48288-enhancing-rag-based-apps-by-constructing-and-leveraging-knowledge-graphs-with-open-weights-llms", "url": "https://pretalx.com/pydata-paris-2024/talk/AZNEHE/", "title": "Enhancing RAG-based apps by constructing and leveraging knowledge graphs with open-weights LLMs", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Graph Retrieval Augmented Generation (Graph RAG) is emerging as a powerful addition to traditional vector search retrieval methods. Graphs are great at representing and storing heterogeneous and interconnected information in a structured manner, effortlessly capturing complex relationships and attributes across different data types. Using open weights LLMs removes the dependency on an external LLM provider while retaining complete control over the data flows and how the data is being shared and stored. In this talk, we construct and leverage the structured nature of graph databases, which organize data as nodes and relationships, to enhance the depth and contextuality of retrieved information to enhance RAG-based applications with open weights LLMs. We will show these capabilities with a demo.", "description": "The idea behind Retrieval Augmented Generation (RAG) applications is to provide Large Language Models (LLMs) with additional context at query time for answering the user\u2019s question. Graph Retrieval Augmented Generation (Graph RAG) is emerging as a powerful addition to these traditional vector search retrieval methods. Graphs are great at representing and storing heterogeneous and interconnected information in a structured manner, effortlessly capturing complex relationships and attributes across diverse data types. In contrast, vector databases often struggle with such structured information, as their strength lies in handling unstructured data through high-dimensional vectors.\nGraph RAGs work as follows:\n- When a user asks a question, it first goes through an embedding model to calculate its vector representation.\n- The next step is to find the most relevant nodes in the database by comparing the cosine similarity of the embedding values of the user\u2019s question and the relevant nodes in the database.\n- Once the relevant nodes are identified, the application is designed to retrieve additional information from the nodes themselves and also by traversing the relationships in the graph.\n- We can combine structured graph data with vector search through unstructured text to achieve the best of both worlds.\n- The context information from these databases is combined with the user question and additional instructions into a prompt that is passed to an LLM to generate the final answer, which is then sent to the user.\n\nUsing open weights LLMs removes the dependency on an external LLM provider while retaining complete control over the data flows and how the data is being shared and stored.\nIn this talk, we first construct a knowledge graph based on the provided documents with an open weights LLM. We then leverage the structured nature of graph databases, which organize data as nodes and relationships, to enhance the depth and contextuality of retrieved information to enhance RAG-based applications with open weights LLMs.\nWe will show these capabilities with a demo. The tools used in the demo will be K\u00f9zu + LanceDB + Llama.cpp + Outlines + LangChain.", "recording_license": "", "do_not_record": false, "persons": [{"code": "CK3BUD", "name": "Alonso Silva", "avatar": "https://pretalx.com/media/avatars/CK3BUD_tGRvZU1.webp", "biography": "Alonso Silva is currently a Researcher on Verifiable AI at Nokia Bell Labs in the Machine Learning and Systems Research Lab. He has previously worked at Safran in the Department of Mathematics and Temporal Data, in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, working with Professor Jean Walrand, at INRIA Paris Rocquencourt working with Dr. Philippe Jacquet and as a Research Consultant/Intern at Bell Labs Headquarters in Murray Hill, New Jersey, working with Dr. Iraj Saniee. He did his Ph.D. at INRIA Sophia-Antipolis under the direction of Dr. Eitan Altman. He obtained his Ph.D. degree in Physics from the \u00c9cole Sup\u00e9rieure d'\u00c9lectricit\u00e9 in June 2010.", "public_name": "Alonso Silva", "guid": "54e89e40-ff9c-5662-8177-d6a1d7d159b6", "url": "https://pretalx.com/pydata-paris-2024/speaker/CK3BUD/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/AZNEHE/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/AZNEHE/", "attachments": []}, {"guid": "0fc168f8-485f-56e2-a6d9-71562a8c7738", "code": "TBREDY", "id": 48461, "logo": null, "date": "2024-09-25T11:40:00+02:00", "start": "11:40", "end": "2024-09-25T12:10:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48461-leveraging-llms-to-build-supervised-datasets-suitable-for-smaller-models", "url": "https://pretalx.com/pydata-paris-2024/talk/TBREDY/", "title": "Leveraging LLMs to build supervised datasets suitable for smaller models", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "For some natural language processing (NLP) tasks, based on your production constraints, a simpler custom model can be a good contender to off-the-shelf large language models (LLMs), as long as you have enough qualitative data to build it. The stumbling block being how to obtain such data? Going over some practical cases, we will see how we can leverage the help of LLMs during this phase of an NLP project. How can it help us select the data to work on, or (pre)annotate it? Which model is suitable for which task? What are common pitfalls and where should you put your efforts and focus?", "description": "Lately, large language models (LLMs) have appeared to make wonders for just about any natural language processing (NLP) task. But for many reasons, one may be unwilling or unable to use such models in production. Which is not a problem if the task can be tackled fairly well with a different kind of model, as long as one has enough qualitative training and testing data.\n\nBut how to obtain said data? This is where LLMs come back into play. We will see how language models can help us create good datasets for different NLP tasks (classification, named entity recognition) with a fraction of the cost, time and pain it would normally require. From data selection to data annotation, we will go over some practical cases. We will try to answer some questions along the way: which model for which task, how much data do I need, and is this the ultimate dream or do you still need to put in a bit of effort? (spoiler alert: you do).", "recording_license": "", "do_not_record": false, "persons": [{"code": "E3DGYC", "name": "Justine BEL-LETOILE", "avatar": "https://pretalx.com/media/avatars/E3DGYC_i4fbinL.webp", "biography": "Justine leads the data science team at HelloWork, a digital provider of employment, recruitment, and training solutions. She spent the last 10+ years enjoying machine learning, python and other data science fun stuff in various fields. Her current work includes a good deal of natural language processing.", "public_name": "Justine BEL-LETOILE", "guid": "836b57de-2583-5bd2-8a33-b718c03ccb7a", "url": "https://pretalx.com/pydata-paris-2024/speaker/E3DGYC/"}, {"code": "8XRSDK", "name": "C\u00e9r\u00e8s Carton", "avatar": null, "biography": "Senior data scientist at HelloWork", "public_name": "C\u00e9r\u00e8s Carton", "guid": "b05d8ccd-0013-5c9e-9561-bb658b903408", "url": "https://pretalx.com/pydata-paris-2024/speaker/8XRSDK/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/TBREDY/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/TBREDY/", "attachments": []}, {"guid": "ab75ce9e-e174-514a-bee7-c24ac09446d7", "code": "MFRP9X", "id": 48125, "logo": null, "date": "2024-09-25T13:50:00+02:00", "start": "13:50", "end": "2024-09-25T14:20:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48125-unveiling-new-maps-of-biology-with-squidpy", "url": "https://pretalx.com/pydata-paris-2024/talk/MFRP9X/", "title": "Unveiling new maps of biology with Squidpy", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Spatial Transcriptomics, named the method of the year by Nature in 2020, offers remarkable visuals of gene expression across tissues and organs, providing valuable insights into biological processes. This talk presents the Squidpy library for analyzing and visualizing spatial molecular data, including demonstrations of gene expression visualization in mouse brain tissue.", "description": "Considered the method of the year by Nature in 2020, Spatial Transcriptomics has unveiled remarkable visuals of how genes are expressed across tissues and organs of living organisms. This breakthrough offers valuable insights to scientists, shedding light on the inner workings of biological processes such as brain function and cancer development. In this talk, we will explore how to build such visuals using Squidpy, a python library for the analysis and visualization of spatial molecular data. First, we will take a look at how Spatial Transcriptomics emerged inside bioinformatics and why it became the method of the year. Next, we will have a brief overview of the Squidpy library, as well as practical demonstrations showcasing how to visualize gene expression in mouse brain tissue. Lastly, we will discuss currently challenges on Spatial Transcriptomics. With this talk, you will gain a better understanding of spatial transcriptomics and will learn how to utilize the Squidpy library for visualizing gene expression in tissue samples.", "recording_license": "", "do_not_record": false, "persons": [{"code": "NPUT8E", "name": "In\u00e1cio Medeiros", "avatar": "https://pretalx.com/media/avatars/NPUT8E_5vBCtaB.webp", "biography": "BSc in Information Technology and PhD in Bioinformatics from the Federal University of Rio Grande do Norte (UFRN). Currently, I am Postdoctoral Researcher at Institut Curie. During my academic journey, I have been extensively involved in research projects focusing on bioinformatics and data analysis. Pythonist and headbanger, with 6 years of experience in employing the Python data science and machine learning stack on bioinformatics and personal projects.", "public_name": "In\u00e1cio Medeiros", "guid": "4a3f8d03-d6d6-5a0a-be2b-e165a34a6c28", "url": "https://pretalx.com/pydata-paris-2024/speaker/NPUT8E/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/MFRP9X/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/MFRP9X/", "attachments": []}, {"guid": "60f57f28-6f8f-5986-9d44-630bbed84597", "code": "MXFAWK", "id": 48585, "logo": null, "date": "2024-09-25T14:25:00+02:00", "start": "14:25", "end": "2024-09-25T14:55:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48585-geoscience-at-massive-scale", "url": "https://pretalx.com/pydata-paris-2024/talk/MXFAWK/", "title": "Geoscience at Massive Scale", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "When scaling geoscience workloads to large datasets, many scientists and developers reach for Dask, a library for distributed computing that plugs seamlessly into Xarray and offers an Array API that wraps NumPy. Featuring a distributed environment capable of running your workload on large clusters, Dask promises to make it easy to scale from prototyping on your laptop to analyzing petabyte-scale datasets.\n\nDask has been the de-facto standard for scaling geoscience, but it hasn\u2019t entirely lived up to its promise of operating effortlessly at massive scale. This comes up in a few ways: \n- Correctly chunking your dataset has a significant impact on Dask\u2019s ability to scale \n- Workers accidentally run out of memory due to: \n  - Data being loaded too eagerly\n  - Rechunking\n  - Unmanaged memory\n\nOver the last few months, Dask has addressed many of those pains and continues to do so through:\n- Improvements to its scheduling algorithms\n- A faster and more memory-stable method for rechunking\n- First-of-its-kind logical optimization layer for a distributed array framework (ongoing)\n\nJoin us as we dive into real-world geoscience workloads, exploring how Dask empowers scientists and developers to run their analyses at massive scale. Discover the impact of improvements made to Dask, ongoing challenges, and future plans for making it truly effortless to scale from your laptop to the cloud.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "SY8ZUX", "name": "Hendrik Makait", "avatar": "https://pretalx.com/media/avatars/SY8ZUX_tZTEaSA.webp", "biography": "Hendrik Makait is a data and software engineer building systems at the intersection of large-scale data management and machine learning. Currently, he works as an Open Source Engineer at Coiled, maintaining and improving Dask and its distributed execution engine. His focus areas include P2P shuffling, which allows shuffling large data at constant memory, and observability with Dask to help users understand and optimize their workloads.", "public_name": "Hendrik Makait", "guid": "9ebee613-8e39-560a-88c1-05d175eff732", "url": "https://pretalx.com/pydata-paris-2024/speaker/SY8ZUX/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/MXFAWK/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/MXFAWK/", "attachments": []}, {"guid": "93f5f0c8-a4bd-5b15-b999-5f6438a6bd5a", "code": "JBUSWA", "id": 48435, "logo": null, "date": "2024-09-25T15:00:00+02:00", "start": "15:00", "end": "2024-09-25T15:30:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48435-xsimd-from-xtensor-to-firefox", "url": "https://pretalx.com/pydata-paris-2024/talk/JBUSWA/", "title": "xsimd:  from xtensor to firefox", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Almost all modern CPU have a vector processing unit, making it possible to write faster code for a large category of problems, at the cost of portability - there a re many different instruction sets in the wild! The xsimd library makes it possible to write portable C++ code that targets different architectures and sub-architectures. The specialization choice can be made at compile-time or at runtime, using a provided dispatching mechanism. Intel, ARM, RiscV and Webassembly are supported, and the library has already been adopted by Xtensor, Pythran, Apache Arrow and Firefox.", "description": "From smartphone running on basic ARM chips to HPC station with the latest Intel CPUs, most modern CPU have a dedicated vector processing unit, making SIMD computing an optimization angle for many applications. Unfortunately, each chipset has its own instruction set, with different flavor across CPU families and within the same CPU family. Performance at the price of portability.\nThe xsimd template library abstracts those difference to provide a common high-level API, bridging the gap between the different archtiectures and providing composite fallbacks when a one-to-one mapping with the ISA doesn't exist. It started as a vectorization backend for the xtensor library, has been adopted by Pythran, Krita, Apache Arrow and lately by Firefox to power its AI-based offline translation engine. It supports all Intel SSE variants, AVX, AVX2 and several AVX-512 extensions. ARM Neon with a few extensions, WebAssembly and RiscV are also supported target. In addition to these architectures, xsimd supports a special scalar target to have a similar API to target scalar and vector code, and an emulated target for testing purpose.\nThis talk will present the xsimd API, showcase a few examples and discuss the difference with intrinsic-based programming and compiler auto vectorization approaches.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZJ3KYE", "name": "Serge \u00ab sans \u00bb Paille", "avatar": null, "biography": "Sometimes a compiler engineer, sometimes a wood chopper, but also a retired wizard of the coast. Enjoy telling stories, be it old legends or modern bit yarns.", "public_name": "Serge \u00ab sans \u00bb Paille", "guid": "5bd420e8-df82-5960-bfc5-31cc4a46703e", "url": "https://pretalx.com/pydata-paris-2024/speaker/ZJ3KYE/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/JBUSWA/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/JBUSWA/", "attachments": []}], "Louis Armand 2 - Ouest": [{"guid": "03a6c2ab-7a06-5d8c-9e3a-beca229f9d6d", "code": "ZQTCPC", "id": 48274, "logo": null, "date": "2024-09-25T09:45:00+02:00", "start": "09:45", "end": "2024-09-25T10:15:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-48274-python-3-12-s-new-monitoring-and-debugging-api", "url": "https://pretalx.com/pydata-paris-2024/talk/ZQTCPC/", "title": "Python 3.12's new monitoring and debugging API", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Python 3.12 introduced a new low-impact monitoring API with [PEP669](https://peps.python.org/pep-0669/), which can be used to implement far faster debuggers than ever before. This talk covers the main advantages of this API and how you can use it to develop small tools.", "description": "Python long lagged a good monitoring and profiling API. It had only the simplistic sys.settrace API, which had a high overhead and couldn't be configured appropriately. The new API, released in October 2023, will change this by offering a proper fine-grained and well-designed monitoring API while also making the commonly used operations fast.\n\nThis talk will give you an introduction to the new API and its design major design decisions and show you how you can use it to write a simple debugger from scratch.", "recording_license": "", "do_not_record": false, "persons": [{"code": "L8PGG9", "name": "Johannes Bechberger", "avatar": "https://pretalx.com/media/avatars/L8PGG9_H9WSrBK.webp", "biography": "Johannes Bechberger is a JVM developer working on profilers and their underlying technology in the SapMachine team at SAP. He started at SAP in 2022 after two years of research studies at the KIT in Java security analyses. His work today comprises many open-source contributions and his blog, where he writes regularly on in-depth profiling and debugging topics and works on his JEP Candidate 435 to add a new profiling API to the OpenJDK. He has been an avid Python user for almost 10 years, with a special interest in type systems and debuggers.\nSince 2023 he's touring through the meet-ups and conferences of Europe, like JavaZone and Devoxx Belgium to speak on various topics.", "public_name": "Johannes Bechberger", "guid": "f7f200dd-5e6f-553f-9f87-db44a6e96662", "url": "https://pretalx.com/pydata-paris-2024/speaker/L8PGG9/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/ZQTCPC/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/ZQTCPC/", "attachments": []}, {"guid": "937b838b-f69c-5f91-896e-d3f677fda6ae", "code": "3NDDQC", "id": 47912, "logo": "https://pretalx.com/media/pydata-paris-2024/submissions/3NDDQC/Screenshot_2024-03-26_at_15.36.39_FMYuVMl.png", "date": "2024-09-25T10:30:00+02:00", "start": "10:30", "end": "2024-09-25T11:00:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-47912-high-performance-data-visualization-for-the-web", "url": "https://pretalx.com/pydata-paris-2024/talk/3NDDQC/", "title": "High Performance Data Visualization for the Web", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Are you looking for a high performance visualization component for the web? Need to filter, sort, pivot, and aggregate static/streaming data in realtime? Daunted by the massive JS ecosystem? In this talk, we\u2019ll build a high performance web frontend using the open source library Perspective.", "description": "The Python ecosystem has ample supply of both web development frameworks, and data visualization components. But despite the maturity of the ecosystem, few datavisualization tools are capable of dealing with large amounts of streaming data. Even fewer are able to perform live aggregations, sorting, and filtering on top of this data.\n\nIn this talk, we will put together a simple but full-featured website using [Perspective](https://perspective.finos.org). Perspective is an open source interactive analytics and data visualization component, which is especially well-suited for large and/or streaming datasets. It is written in C++ and Rust with bindings to both Python and WebAssembly, making it ideal for data-intensive applications. It comes with a variety of visualization plugins, including a datagrid and various charts. Additionally, it comes with a Jupyter widget, which allows developers to iterate quickly with a clear pathway to their production website.\n\nWe will start with a simple [FastAPI](https://fastapi.tiangolo.com)-based website and some static data. In a few lines of code, we will have the website up and running. Next, we will demonstrate some of the core features of Perspective - pivoting, sorting, filtering, the various visualization plugins, cross-filtering (using one table as a filter on other tables), and computed columns. After this, we will pull in some streaming data and show how the functionality of Perspective demonstrated updates in realtime alongside the data. Finally, we'll crank the speed of updates to the limit.\n\nBy the end of this talk, the audience will know how to use Perspective and how to incorporate it into their own applications for both static and streaming data, either as a simple but high performance datagrid or as a full featured set of interconnected visualization components.", "recording_license": "", "do_not_record": false, "persons": [{"code": "TM3KV3", "name": "Tim Paine", "avatar": "https://pretalx.com/media/avatars/TM3KV3_c6kvP3r.webp", "biography": "Tim is a Quantitative Developer at Cubist Systematic Strategies and an adjunct professor in the Computer Science Department at Columbia University.", "public_name": "Tim Paine", "guid": "2db096c1-98d8-57f5-90ac-450532c61f08", "url": "https://pretalx.com/pydata-paris-2024/speaker/TM3KV3/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/3NDDQC/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/3NDDQC/", "attachments": []}, {"guid": "4986a15a-4690-59cc-bf34-1bf82e860712", "code": "XKGQXH", "id": 48417, "logo": null, "date": "2024-09-25T11:05:00+02:00", "start": "11:05", "end": "2024-09-25T11:35:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-48417-elevating-data-stories-exploring-quarto-dashboard-for-impactful-and-visual-communication", "url": "https://pretalx.com/pydata-paris-2024/talk/XKGQXH/", "title": "Elevating Data Stories: Exploring Quarto Dashboard for impactful and visual communication", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Embark on a journey to explore how Quarto Dashboard can enhance the narrative of your analysis from your Jupyter Notebook. This talk will show how to create cool interactive charts and graphs that bring your data to life, by using Quarto - an open-source scientific and technical publishing system. \n\nLearn how to make your data communications more engaging and dynamic using Quarto Dashboard. Practical examples and simple explanations will guide you through the process, making it easy to understand and apply to your projects.", "description": "**Content of the talk:**\nIn this session, we'll first learn briefly about Quarto and how it could be used with Jupyter. Then, we'll explore the features of Quarto Dashboard and how it can transform your linear data analysis into dynamic visual stories. You'll see how easy it is to customize your data visualizations, organize results in advanced layouts, add interactive elements, and create eye-catching dashboards. The presentation will show an example of a dashboard created with Quarto and Jupyter.\n\nBy the end of the talk, you'll have the tools and knowledge to elevate your data presentations and make them more impactful and engaging. \n\n**Target Audience:**\nThis talk is for anyone interested in improving their data visualization skills, from beginners to experienced professionals. Whether you're a data scientist, analyst, educator, or just curious about data storytelling, you'll find value in learning how Quarto Dashboard can enhance data presentations and analysis simply and practically. No prior Quarto knowledge is needed.\n\n**Key Takeaways**: \n- Learn how Quarto works effectively with Jupyter Notebooks to create Dashboard output to publish\n- Discover the customization options available in Quarto Dashboard\n- Understand how Quarto Dashboard can enhance your data storytelling and analysis processes", "recording_license": "", "do_not_record": false, "persons": [{"code": "9DWJKU", "name": "Christophe Dervieux", "avatar": "https://pretalx.com/media/avatars/9DWJKU_ufjs9GU.webp", "biography": "Open Source Software Engineer at Posit PBC (<https://posit.co/>). Working remotely from France.\nProudly focusing on Quarto (<https://quarto.org/>) - an open-source scientific and technical publishing system.", "public_name": "Christophe Dervieux", "guid": "0a91f0c9-d8a4-599b-9635-c50a894f7546", "url": "https://pretalx.com/pydata-paris-2024/speaker/9DWJKU/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/XKGQXH/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/XKGQXH/", "attachments": []}, {"guid": "82e612ca-2045-5736-869d-61d8c622f7c4", "code": "DFAEZ7", "id": 48469, "logo": null, "date": "2024-09-25T11:40:00+02:00", "start": "11:40", "end": "2024-09-25T12:10:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-48469-solara-pure-python-web-apps-beyond-prototypes-and-dashboards", "url": "https://pretalx.com/pydata-paris-2024/talk/DFAEZ7/", "title": "Solara: Pure Python web apps beyond prototypes and dashboards", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Many Python frameworks are suitable for creating basic dashboards or prototypes but struggle with more complex ones. Taking lessons from the JavaScript community, the experts on building UI\u2019s, we created a new framework called Solara. Solara scales to much more complex apps and compute-intensive dashboards. Built on the Jupyter stack, Solara apps and its reusable components run in the Jupyter notebook and on its own production quality server based on Starlette/FastAPI.\n\nSolara has a declarative API that is designed for dynamic and complex UIs yet is easy to write. Reactive variables power our state management, which automatically triggers rerenders. Our component-centric architecture stimulates code reusability, and hot reloading promotes efficient workflows. With our rich set of UI and data-focused components, Solara spans the entire spectrum from rapid prototyping to robust, complex dashboards.", "description": "Solara bridges the gap between many Python web frameworks suitable for prototypes and full-stack web apps. We have looked at web frameworks in the JavaScript community, such as ReactJS, SolidJS, VueJS, and learned how they scale to larger apps.\nDeclarative UIs and a dedicated state management system are the core of the solutions.\nWe have built Solara on the Jupyter stack (ipywidgets) to avoid re-inventing the wheel. It also means we can reuse many of the widgets already created (such as anywidget based libraries).\n\nPlanned outline of my talk:\n * Introduction/context\n * The problem: what is the problem we are trying to solve\n * Introducing Solara: general high-level overview of Solara\n * Solara in depth: go over the problems solara solves, and demo them with code (live demo in Jupyter Notebook)\n * Solara use cases: how is Solara currently used, real-life examples to show it\n* Quality focus: highlight the testing and typing support\n* Conclude\n\nThe key takeaway: With Solara, you can create complex dashboards in Python beyond what you previously thought possible.\n\n\nSolara docs: https://solara.dev/\nSolara GitHub: https://github.com/widgetti/solara/\n\n\nI've given popular talks at SciPy before:\nhttps://www.youtube.com/watch?v=VtchVpoSdoQ - Dashboarding with Jupyter Notebooks, Voila and Widgets | SciPy 2019 | M. Breddels and M. Renou\nhttps://www.youtube.com/watch?v=hOKa8klJPyo - Interactive 3D Visualization in Jupyter | SciPy 2018 | Maarten Breddels\nhttps://www.youtube.com/watch?v=ELtjRdPT8is - Vaex: Out of Core Dataframes for Python | SciPy 2019 | Maarten Breddels", "recording_license": "", "do_not_record": false, "persons": [{"code": "38EYUA", "name": "Maarten Breddels", "avatar": "https://pretalx.com/media/avatars/38EYUA_GCSIkAo.webp", "biography": "Maarten Breddels is an entrepreneur and ex-scientist mainly working with Python, C++, and Javascript in the Jupyter ecosystem. He is the creator of Solara, ipyvolume, and Vaex and Co-founder of Widgetti. His expertise includes fast numerical computation, API design, 3D visualization, and building data apps. He has a Bachelor's in ICT, a Master's, and Ph.D. in Astronomy, and he likes to solve real problems.", "public_name": "Maarten Breddels", "guid": "f04edad1-08aa-56f2-b189-a7e85b0209b1", "url": "https://pretalx.com/pydata-paris-2024/speaker/38EYUA/"}, {"code": "JYF3CX", "name": "Iisakki Rotko", "avatar": "https://pretalx.com/media/avatars/JYF3CX_n9XxYRC.webp", "biography": "Iisakki Rotko is a Software Engineer at Widgetti, where he focuses on advancing Python frameworks for complex dashboard creation through his work on Solara. With a background in theoretical physics from the University of Groningen, Iisakki combines his analytical skills with knowledge of Python and TypeScript to develop innovative tools at the interface between data and users, within the Jupyter ecosystem and beyond.", "public_name": "Iisakki Rotko", "guid": "6e8909e1-21c0-50ed-b874-b4a7112a0276", "url": "https://pretalx.com/pydata-paris-2024/speaker/JYF3CX/"}], "links": [{"title": "Slides online", "url": "https://docs.google.com/presentation/d/12Y4YJlJ_YC2DWiYvIZljySwSMPqE9oFuHF_YQxMBFEg/edit?usp=sharing", "type": "related"}], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/DFAEZ7/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/DFAEZ7/", "attachments": []}, {"guid": "e98a78f7-c31f-528c-b6d1-cffd4dac3699", "code": "WTXRDB", "id": 48448, "logo": null, "date": "2024-09-25T13:50:00+02:00", "start": "13:50", "end": "2024-09-25T14:20:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-48448-bridging-the-worlds-pixi-reimplements-pip-and-conda-in-rust", "url": "https://pretalx.com/pydata-paris-2024/talk/WTXRDB/", "title": "Bridging the worlds: pixi reimplements pip and conda in Rust", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Pixi goes further than existing conda-based package managers in many ways:\n\n* From scratch implemented in Rust and ships as a single binary\n* Integrates a new SAT solver called resolvo\n* Supports lockfiles like `poetry` / `yarn` / `cargo` do\n* Cross-platform task system (simple `bash`-like syntax)\n* Interoperability with PyPI packages by integrating `uv`\n* It's 100% open-source with a permissive licence\n\nWe\u2019re looking forward to take a deep-dive together into what conda and PyPI packages are and how we are seamlessly integrating the two worlds in pixi.\n\nWe will show you how you can easily setup your new project using just one configuration file and always have a reproducible setup in your pocket. Which means that it will always run the same for your contributors, user and CI machine ( no more \"but it worked on my machine!\" ).\n\nUsing pixi's powerful cross-platform task system you can replace your `Makefile` and a ton of developer documentation with just `pixi run task`!\n\nWe\u2019ll also look at benchmarks and explain more about the difference between the conda and pypi ecosystems.\n\nThis talk is for everyone who ever dealt with [dependency hell](https://prefix.dev/blog/launching_pixi).\n\nMore information about Pixi:\n\nhttps://pixi.sh\nhttps://prefix.dev\nhttps://github.com/prefix-dev/pixi", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "VSBQXQ", "name": "Nichita Morcotilo", "avatar": "https://pretalx.com/media/avatars/VSBQXQ_vEDCleN.webp", "biography": "Software Developer with a passion for package management systems and focused on developer experience. Previously worked on high-load online marketplace, fintech trading platform and monorepo management tool.", "public_name": "Nichita Morcotilo", "guid": "19efc148-6fdd-5aff-9047-6dc163c8c260", "url": "https://pretalx.com/pydata-paris-2024/speaker/VSBQXQ/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/WTXRDB/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/WTXRDB/", "attachments": []}, {"guid": "fb1a492b-8dad-5eb6-9d4c-4d4f4c724250", "code": "837ZAH", "id": 48524, "logo": null, "date": "2024-09-25T14:25:00+02:00", "start": "14:25", "end": "2024-09-25T14:55:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-48524-unveiling-mamba-2-0-the-future-of-fast-package-management", "url": "https://pretalx.com/pydata-paris-2024/talk/837ZAH/", "title": "Unveiling Mamba 2.0: The Future of Fast Package Management", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "In this presentation, we introduce Mamba 2.0, the latest version of the multi-platform, language-agnostic package manager that has garnered significant adoption within the scientific open-source community for its speed and efficiency.", "description": "Initially developed as a Python wrapper around OpenSUSE's libsolv SAT solver, Mamba has evolved into a full-fledged native package manager, compatible with Conda packages. Its recent integration into the conda-forge project and conda itself has enabled them to scale beyond previous limits.\n\nIn this presentation, we will unveil the key enhancements and innovations in Mamba 2.0. We will explore the extensive refactoring process that transformed Mamba into a flexible framework, beyond the popular command-line utility, and delve into the main components of the core library. \n\nWe will also highlight new key features such as support for OCI registry and the ability to integrate different SAT solvers.\n\nThe talk will conclude with a glimpse into the future of Mamba, with a particular focus on software supply chain security. This includes plans for package signing and the implementation of a Software Bill Of Materials (SBOM), further solidifying Mamba's commitment to secure and reliable package management.", "recording_license": "", "do_not_record": false, "persons": [{"code": "PHNY9R", "name": "Julien Jerphanion", "avatar": "https://pretalx.com/media/avatars/PHNY9R_dNPRwVe.webp", "biography": "Julien is a Scientific Software Engineer at QuantStack. He holds a MSc. Computer Science & Engineering from Universit\u00e9 de Technologie de Compi\u00e8gne and a MSc. Applied Mathematics, Computer Vision and Machine Learning from \u00c9cole Normale Sup\u00e9rieure Paris-Saclay.\n\nPrior to joining QuantStack, Julien worked as a Research Software Engineer at Inria.\n\nJulien's principal interests sit at the intersection of Applied Mathematics, Computer Science, and Software. Julien also co-maintains scikit-learn on his decreasing free-time.", "public_name": "Julien Jerphanion", "guid": "d3a47b5e-678e-5356-8de6-2e172edf3a18", "url": "https://pretalx.com/pydata-paris-2024/speaker/PHNY9R/"}, {"code": "FK8VW8", "name": "Johan Mabille", "avatar": "https://pretalx.com/media/avatars/FK8VW8_bUxNofz.webp", "biography": "Johan Mabille is a Technical Director specialized in high-performance computing in C++. He holds master's degree in computer science from Centrale-Supelec.\n\nAs an open source developer, Johan co-authored xtensor, xeus, and xsimd. He is also involved in the development of mamba. Prior to joining QuantStack, Johan was a quant developer at HSBC.", "public_name": "Johan Mabille", "guid": "4ad7bd48-803e-5661-863c-cb3fcbce68da", "url": "https://pretalx.com/pydata-paris-2024/speaker/FK8VW8/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/837ZAH/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/837ZAH/", "attachments": []}, {"guid": "d6fc0ea5-58a7-54b8-85b1-bd6a2148bf53", "code": "9RZ8MH", "id": 48525, "logo": null, "date": "2024-09-25T15:00:00+02:00", "start": "15:00", "end": "2024-09-25T15:30:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-48525-on-the-structure-and-reproducibility-of-python-packages-data-crunch", "url": "https://pretalx.com/pydata-paris-2024/talk/9RZ8MH/", "title": "On the structure and reproducibility of Python packages - data crunch", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Did you know that all top PyPI packages declare their 3rd party dependencies? In contrast, only about 53% of scientific projects do the same. The question arises: How can we reproduce Python-based scientific experiments if we're unaware of the necessary libraries for our environment?\nIn this talk, we delve into the Python packaging ecosystem and employ a data-driven approach to analyze the structure and reproducibility of packages. We compare two distinct groups of Python packages: the most popular ones on PyPI, which we anticipate to adhere more closely to best practices, and a selection from biomedical experiments. Through our analysis, we uncover common development patterns in Python projects and utilize our open-source library, FawltyDeps, to identify undeclared dependencies and assess the reproducibility of these projects.\nThis discussion is especially valuable for enthusiasts of clean Python code, as well as for data scientists and engineers eager to adopt best practices and enhance reproducibility. Attendees will depart with actionable insights on enhancing the transparency and reliability of their Python projects, thereby advancing the cause of reproducible scientific research.", "description": "Python has emerged as one of the most popular programming languages, with an extensive ecosystem of packages and projects catering to diverse domains. However, the structure and reproducibility of Python projects vary significantly, posing challenges for developers and researchers alike. In this talk, we perform a meta-analysis of Python packages in two different domains to gain a better understanding of wide-spread patterns and quantify the impact of missing or mis-configured dependencies on reproducibility. \nThe FawltyDeps team was driven by a nagging question: How reproducible are Python packages and is there a discernible difference between top packages and those developed for scientific research? So we did what any data-loving person would do - we collected data and analyzed it using the core logic of FawltyDeps and indispensable Jupyter notebooks, e.g. biomedical data analysis. Our analysis includes packages from biomedical domain, drawing on the article by Sheeba and Mietchen, and for top PyPI packages, we used public BigQuery data.\nOur goal is to compare and contrast data-related projects with the top packages to unearth insights into the significant ways these two groups differ, both internally and in relation to each other, in terms of structure and dependency configuration. For instance, we found that nearly 20% of data science projects store all code and notebook files in the main directory, indicating a trend. This latter sample showcases data science projects crafted by developers of varying expertise levels, in which we anticipate greater variability in both structure and reproducibility.\n\nOutline:\nIntroduction (0-2\u2019):\nWe introduce the speakers and provide an overview of the talk's objectives.\nReproducibility of Python Projects (3-10\u2019):\nWe delve into the importance of reproducibility in Python projects and discuss common challenges faced by developers.\nMethodology and Data Collection (11-15\u2019):\nWe outline the experimental methodology employed, including the selection of biomedical experiments and PyPI packages for analysis and the different types of analysis done on those packages.\nResults and Insights (16-23\u2019):\nWe present the findings of our analysis, highlighting common patterns in Python project development and assessing their reproducibility using FawltyDeps.\nKey Takeaways (24-25\u2019):\nWe emphasize the key lessons learned from our analysis, particularly the importance of structured project development and dependency declarations.\nQuestion and Answer Session (26-30\u2019):", "recording_license": "", "do_not_record": false, "persons": [{"code": "DNCKCS", "name": "Maria Knorps", "avatar": "https://pretalx.com/media/avatars/DNCKCS_WyTl55M.webp", "biography": "Maria's professional goal is to improve the environment by understanding it first in\n  the language of mathematics and then applying the gained knowledge.\n  After graduating in applied mathematics, Maria began research on the two-phase turbulent flows.\n  Knowledge of mathematical modeling helped her better understand the small-scale physical effects \n  and allowed her to more accurately model two-phase turbulence while reducing computational costs.\n\n  After completing her PhD, Maria began to work as a data scientist. \n  She was responsible for all stages of data processing, from creating ETL pipelines, through modeling \n  to visualization of the results and leading 2-5 people projects.\n  Her inclination towards the implementation and design aspects gravitated her towards functional programming.\n  She integrated Haskell into parts of the data processing pipelines, finding its type system and\n  expressiveness more akin to mathematical language. Maria is also dedicated to maintaining neat,\n  reusable, and well-documented code.\n\n  Outside of her technical pursuits, Maria is passionate about promoting diversity in the IT industry\n  and inspiring girls and women to engage in programming. Balancing her career with being a mother of three,\n  she finds limited but cherished time for personal hobbies. When the opportunity arises,\n  Maria enjoys the thrill of motorcycle rides beyond the city limits.", "public_name": "Maria Knorps", "guid": "731efbea-53d3-5dc5-b5e3-7d9075a3491a", "url": "https://pretalx.com/pydata-paris-2024/speaker/DNCKCS/"}, {"code": "REHGCK", "name": "Zhihan Zhang", "avatar": "https://pretalx.com/media/avatars/REHGCK_lNfEDCU.webp", "biography": "Zhihan is a data scientist/engineer with a rich academic background in\n  pure mathematics. Having earned a PhD in Geometric Topology and Probabilities\n  from Telecom Paris, she defined random walks on simplicial complexes and\n  applied machine learning algorithms for in-depth analysis.\n\n  At Tweag (an Open Source Program Office of Modus create), \n  Zhihan brings mathematical rigor to the forefront of\n  data science and data engineering. She is actively engaged in developing\n  innovative data engineering solutions and crafting compelling data\n  visualizations, all while embracing the challenges of cloud deployment.", "public_name": "Zhihan Zhang", "guid": "0feb08eb-1bf9-5bc1-822a-1a8d17a99983", "url": "https://pretalx.com/pydata-paris-2024/speaker/REHGCK/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/9RZ8MH/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/9RZ8MH/", "attachments": [{"title": "Another result - a comparison of usage of files where 3rd party dependencies are declared: requirements.txt, setup.py, setup.cfg, pyproject.toml and their combinations.", "url": "/media/pydata-paris-2024/submissions/9RZ8MH/resources/declaration_files_U9EYNKn.png", "type": "related"}]}]}}, {"index": 2, "date": "2024-09-26", "day_start": "2024-09-26T04:00:00+02:00", "day_end": "2024-09-27T03:59:00+02:00", "rooms": {"Gaston Berger": [{"guid": "cb24b89c-4f45-569e-a35c-0dd0ac4ed494", "code": "U7CM7L", "id": 51865, "logo": null, "date": "2024-09-26T09:00:00+02:00", "start": "09:00", "end": "2024-09-26T09:45:00+02:00", "duration": "00:45", "room": "Gaston Berger", "slug": "pydata-paris-2024-51865-keynote-building-with-mistral", "url": "https://pretalx.com/pydata-paris-2024/talk/U7CM7L/", "title": "Keynote: Building with Mistral", "subtitle": "", "track": null, "type": "Keynote", "language": "en", "abstract": "In the rapidly evolving landscape of Artificial Intelligence (AI), open source and openness AI have emerged as crucial factors in fostering innovation, transparency, and accountability. Mistral AI's release of the open-weight Mistral 7B model has sparked significant adoption and demand, highlighting the importance of open-source and customization in building AI applications. This talk focuses on the Mistral AI model landscape, the benefits of open-source and customization, and the opportunities for building AI applications using Mistral models.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "CD7N8F", "name": "Sophia Yang", "avatar": "https://pretalx.com/media/avatars/CD7N8F_mQUkbgu.webp", "biography": "Sophia Yang is the Head of Developer Relations at Mistral AI, where she leads developer education, developer ecosystem partnerships, and community engagement. She is passionate about the AI community and the open-source community, and she is committed to empower community growth and learning.", "public_name": "Sophia Yang", "guid": "55f20ff9-b838-5a0f-ae0d-7ab9884b087e", "url": "https://pretalx.com/pydata-paris-2024/speaker/CD7N8F/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/U7CM7L/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/U7CM7L/", "attachments": []}, {"guid": "9949103f-6bd5-5366-b188-193a417ee2f1", "code": "Y9S73X", "id": 47653, "logo": "https://pretalx.com/media/pydata-paris-2024/submissions/Y9S73X/create-kpi-dashboard_CvndtgL.jpeg", "date": "2024-09-26T09:45:00+02:00", "start": "09:45", "end": "2024-09-26T10:15:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-47653-unpack-business-metrics-to-explain-their-evolution", "url": "https://pretalx.com/pydata-paris-2024/talk/Y9S73X/", "title": "Unpack business metrics to explain their evolution", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "One of the more mundane tasks in the business analytics world is to measure KPIs: averages, sums, ratios, etc. Typically, these are measured period over period, to see how they trend. If you're a data analyst, you've likely been asked to debug/explain a metric, because a stakeholder wants to understand why a number has changed.\n\nThis topic isn't well grounded theory, and the answers we come up with can be lacklustre. In this talk, we discuss solutions to this very common topic. We will look at a methodology we have developed at Carbonfact, and the opensource Python tool we are sharing.", "description": "This talk is for all the people who perform analytics in their daywork. The goal of the talk is to add a tool to your belt. You'll leave the talk with a robust framework for answering questions from your stakeholders. For instance, you'll be able to attribute the change in a metric to the drivers of said metric, down to the decimal point.\n\nThe presentation will present the (simple) maths of the methodology we propose. It's a simplified version of the Blinder-Oaxaca decomposition framework. We will then present the Python toolbox we are open-sourcing, along with a tutorial. Finally, we will discuss what are the best ways to share such methods in the current data tooling landscape (e.g. with the rise of DuckDB and Polars).\n\nThe talk will be given by Max Halford, who is Head of Data at Carbonfact. Max has already given a talk at PyData Amsterdam, and regularly gives talks at different venues.", "recording_license": "", "do_not_record": false, "persons": [{"code": "CS9B3G", "name": "Max Halford", "avatar": "https://pretalx.com/media/avatars/CS9B3G_8XLy4Zh.webp", "biography": "https://maxhalford.github.io/bio/", "public_name": "Max Halford", "guid": "e2796bf6-6a8a-549d-bbd7-50b8e838b865", "url": "https://pretalx.com/pydata-paris-2024/speaker/CS9B3G/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/Y9S73X/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/Y9S73X/", "attachments": []}, {"guid": "011b2fd8-a12b-5017-aa61-852991d4d19c", "code": "ZLJZRT", "id": 47950, "logo": null, "date": "2024-09-26T10:30:00+02:00", "start": "10:30", "end": "2024-09-26T11:00:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-47950-is-your-marketing-effective-let-bayes-decide", "url": "https://pretalx.com/pydata-paris-2024/talk/ZLJZRT/", "title": "Is your marketing effective? Let Bayes decide!", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Understanding the effectiveness of various marketing channels is crucial to maximise the return on investment (ROI). However, the limitation of third-party cookies and an ever-growing focus on privacy make it difficult to rely on basic analytics. This talk discusses a pioneering project where a **Bayesian model** was employed to assess the marketing media mix effectiveness of **[WeRoad](https://www.weroad.it/)**, the fastest-growing Italian tour operator.\n\nThe Bayesian approach allows for the incorporation of prior knowledge, seamlessly updating it with new data to provide robust, actionable insights. This project leveraged a Bayesian model to unravel the complex interactions between marketing channels such as online ads, social media, and promotions. We'll dive deep into how the Bayesian model was designed, discussing how we provided the AI system with expert knowledge, and presenting how **delays and saturation** were modelled.\n\nWe will also tackle aspects of the technical implementation, discussing how Python, **[PyMC](https://www.pymc.io/welcome.html)**, and Streamlit provided us with the all the tools we needed to develop an effective, efficient, and user-friendly system.\n\nAttendees will walk away with:\n\n- A simple understanding of the Bayesian approach and why it matters.\n- Concrete examples of the transformative impact on WeRoad's marketing strategy.\n- A blueprint to harness predictive models in their business strategies.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "9CGGBC", "name": "Emanuele Fabbiani", "avatar": "https://pretalx.com/media/avatars/9CGGBC_ieyf8P1.webp", "biography": "Engineer by education, Data Scientist by choice, researcher and lecturer by passion. \n\n[Emanuele](https://www.linkedin.com/in/emanuelefabbiani/) earned his PhD in AI by researching time series forecasting in the energy field. He was a guest researcher at EPFL Lausanne, and he's now the Head of AI at [xtream](https://xtreamers.io/), where he solves business problems with AI. \n\nHe published 8 papers in international journals, presented and organized tracks and workshops at international conferences, including AMLD Lausanne, ODSC London, WeAreDevelopers Berlin, PyCon Florence, and lectured in Italy, Switzerland, and Poland.", "public_name": "Emanuele Fabbiani", "guid": "e7e0690a-665c-5ca6-a2bd-ff070fceeedf", "url": "https://pretalx.com/pydata-paris-2024/speaker/9CGGBC/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/ZLJZRT/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/ZLJZRT/", "attachments": []}, {"guid": "6b213133-37d4-53f9-ba04-058cdf717715", "code": "LTFWZK", "id": 48241, "logo": null, "date": "2024-09-26T11:05:00+02:00", "start": "11:05", "end": "2024-09-26T11:35:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-48241-catering-causal-inference-an-introduction-to-metalearners-a-flexible-metalearner-library-in-python", "url": "https://pretalx.com/pydata-paris-2024/talk/LTFWZK/", "title": "Catering Causal Inference: An Introduction to 'metalearners', a Flexible MetaLearner Library in Python", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Discover metalearners, a cutting-edge Python library designed for Causal Inference with particularly flexible and user-friendly MetaLearner implementations. metalearners leverages the power of conventional Machine Learning estimators and molds them into causal treatment effect estimators. This talk is targeted towards data professionals with some Python and Machine Learning competences, guiding them to optimizing interventions such as 'Which potential customers should receive a voucher to optimally allocate a voucher budget?' or 'Which patients should receive which medical treatment?' based on causal interpretations.", "description": "We will introduce metalearners, a particularly flexible and user-friendly MetaLearner implementation for Causal Inference in the Python ecosystem. \n\nThrough this session, we aim to demonstrate how metalearners, coupled with **standard ML estimators**, can produce **robust causal models** for **treatment effects** in light of optimizing interventional **decision making** or policy learning. Unlike existing implementations, it provides a high degree of flexibility: using elaborate pandas and polars features, convenient investigation of base estimators or the ability of reusing pre-trained models are some of its advantages.\n\n### Background\n\nIn many scenarios in which we intervene with the real world, e.g. administering drugs to patients or distributing vouchers to potential clients, correlation in observational data and prediction models just don't quite do the trick. Conceptually and methodologically, Causal Inference offers the appropriate tools for such scenarios. Yet, practice hasn't quite kept up with the developments on paper. In particular, implementations of MetaLearners - a relatively recent and exciting development in the field of Causal Inference - currently still lack user-friendly, flexible and efficient implementations. metalearners seeks to fill this gap.\n\n\n### Agenda\n- Why care about treatment effect estimation? (5')\n- Introduction to MetaLearners for treatment effect estimation (5')\n- Some shortcomings with existing libraries (5')\n- Demo (10')\n- Q&A (5')\n\n### Target Audience\nThis talk aims at Data Scientists, ML engineers, AI researchers, and Analysts with some Python familiarity, an understanding of basic Machine Learning concepts and at least some curiosity about causality.\n\n### Key Takeaways\n- Insights into merging Causal Inference with standard Machine Learning models\n- Understanding of metalearners' role in flexible and scalable MetaLearner implementations", "recording_license": "", "do_not_record": false, "persons": [{"code": "TVD78Z", "name": "Kevin Klein", "avatar": "https://pretalx.com/media/avatars/TVD78Z_bUlbXj9.webp", "biography": "Kevin is a Data Scientist at QuantCo, where he's been working with insurances on fraud detection, risk modelling and policy learning. In the past two years he's gotten into the field of Causal Inference. Prior to joining QuantCo, he majored in Computer Science at ETH in Zurich - focusing on Theoretical Computer Science and Machine Learning. He's passionate about Open Source and is the organizer of the PyData Zurich meetup.", "public_name": "Kevin Klein", "guid": "28b34b0c-2169-5dba-936d-8a880bb454fd", "url": "https://pretalx.com/pydata-paris-2024/speaker/TVD78Z/"}, {"code": "XKT8XV", "name": "Francesc Mart\u00ed Escofet", "avatar": "https://pretalx.com/media/avatars/XKT8XV_GFsoMWE.webp", "biography": "Francesc is a MSc Data Science student at ETH Z\u00fcrich. Before that, he graduated in Maths & CS at the Polytechnic University of Catalonia (UPC) with a year abroad at Columbia University. He joined QuantCo as an intern in February 2024 to work on causal inference and its applications to insurance and fraud detection.", "public_name": "Francesc Mart\u00ed Escofet", "guid": "872e368f-6a35-5e5c-b7bf-2b7070e25883", "url": "https://pretalx.com/pydata-paris-2024/speaker/XKT8XV/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/LTFWZK/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/LTFWZK/", "attachments": []}, {"guid": "a511c502-3d1c-5c1d-b099-b5bb09c60f96", "code": "VJWXQL", "id": 48522, "logo": null, "date": "2024-09-26T11:40:00+02:00", "start": "11:40", "end": "2024-09-26T12:10:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-48522-dreadful-frailties-in-propensity-score-matching-and-how-to-fix-them", "url": "https://pretalx.com/pydata-paris-2024/talk/VJWXQL/", "title": "Dreadful Frailties in Propensity Score Matching and How to Fix Them.", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "In their seminal paper \"Why propensity scores should not be used for matching,\" King and Nielsen (2019) highlighted the shortcomings of Propensity Score Matching (PSM). Despite these concerns, PSM remains prevalent in mitigating selection bias across numerous retrospective medical studies each year and continues to be endorsed by health authorities. Guidelines to mitigating these issues have been proposed, but many researchers encounter difficulties in both adhering to these guidelines and in thoroughly documenting the entire process.\n\nIn this presentation, I show the inherent variability in outcomes resulting from the commonly accepted validation condition of Standardized Mean Difference (SMD) below 10%. This variability can significantly impact treatment comparisons, potentially leading to misleading conclusions. To address this issue, I introduce A2A, a novel metric computed on a task specifically designed for the problem at hand. By integrating A2A with SMD, our approach substantially reduces the variability of predicted Average Treatment Effects (ATE) by up to 90% across validated matching techniques.\n\nThese findings collectively enhance the reliability of PSM outcomes and lay the groundwork for a comprehensive automated bias correction procedure. Additionally, to facilitate seamless adoption across programming languages, I have integrated these methods into \"popmatch,\" a Python package that not only incorporates these techniques but also offers a convenient Python interface for R's MatchIt methods.", "description": "This presentation aims at raising awareness on the pressing challenge of evaluating bias correction methods, which suffers from a lack of ground truth for validation. While validation typically relies on expert opinion,we propose a novel metric and lay the groundwork for a comprehensive automated pipeline. This pipeline aims to offer consistent results across experiments, even if not optimal, thereby addressing the current variability in outcomes.\n\nPrimarily geared towards practitioners in the medical field who utilize propensity score matching, this talk offers practical examples and steers clear of excessive theoretical discussions. Nevertheless, the core message is applicable to various bias correction methods across different domains. The presentation aims to identify the problem, provides a rudimentary solution ready for refinement, and advocates for public engagement in the project.\n\nThe talk is structured as follows:\n\n- Identifying the Problem: Using real and synthetic datasets, we demonstrate how different population matching techniques can produce varying results, highlighting the need for a robust evaluation method.\n\n- Introduction of Ground Truth in Bias Correction: We demonstrate how existing data can be leveraged to construct artificial problems with known solutions, offering a benchmark for evaluation.\n\n- Crafting a Metric: We present A2A, a novel metric designed to evaluate bias correction methods based on the repeated creation of synthetic problems. We employ this metric to gain a deeper understanding of the phenomenon.\n\n- Future Perspectives: We discuss the limitations of the proposed metric and avenues for refinement. Additionally, we contextualize this endeavor within the broader framework of building a fully automated bias correction engine, highlighting remaining tasks to encourage contributions.", "recording_license": "", "do_not_record": false, "persons": [{"code": "3RAES7", "name": "Alexandre Abraham", "avatar": "https://pretalx.com/media/avatars/3RAES7_crbhDpF.webp", "biography": "Lead data scientist at Implicity.", "public_name": "Alexandre Abraham", "guid": "177a6975-3731-5ac6-ac7b-0d2c0ef125b2", "url": "https://pretalx.com/pydata-paris-2024/speaker/3RAES7/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/VJWXQL/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/VJWXQL/", "attachments": []}, {"guid": "8a442fba-5a39-59df-bb2a-a6d19712b6ef", "code": "QQSZCJ", "id": 48546, "logo": null, "date": "2024-09-26T13:50:00+02:00", "start": "13:50", "end": "2024-09-26T14:20:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-48546-boosting-ai-reliability-uncertainty-quantification-with-mapie", "url": "https://pretalx.com/pydata-paris-2024/talk/QQSZCJ/", "title": "Boosting AI Reliability: Uncertainty Quantification with MAPIE", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "MAPIE (Model Agnostic Prediction Interval Estimator) is your go-to solution for managing uncertainties and risks in machine learning models. This Python library, nestled within scikit-learn-contrib, offers a way to calculate prediction intervals with controlled coverage rates for regression, classification, and even time series analysis. But it doesn't stop there - MAPIE can also be used to handle more complex tasks like multi-label classification and semantic segmentation in computer vision, ensuring probabilistic guarantees on crucial metrics like recall and precision. MAPIE can be integrated with any model - whether it's scikit-learn, TensorFlow, or PyTorch. Join us as we delve into the world of conformal predictions and how to quickly manage your uncertainties using MAPIE.\n\nLink to Github: https://github.com/scikit-learn-contrib/MAPIE", "description": "This talk introduces MAPIE, an open-source Python library designed to quantify uncertainties and control risks in machine learning models. Learn how to compute conformal prediction intervals and control risks in various tasks such as regression, classification, time series, and even complex tasks like multi-label classification and semantic segmentation.\n\nWe will begin by discussing the importance of uncertainty quantification and risk control in machine learning models. Then, we will dive into the key features of MAPIE, including:\n\n1. Computing conformal prediction intervals for regression, classification, and time series tasks with guaranteed marginal coverage rates.\n2. Controlling risks for complex tasks such as multi-label classification and semantic segmentation in computer vision.\n3. Wrapping any machine learning model (scikit-learn, TensorFlow, PyTorch, etc.) with a scikit-learn-compatible wrapper for uncertainty quantification and risk control.\n\nThroughout the talk, we will demonstrate MAPIE's capabilities with practical examples and code snippets. Attendees will learn how to apply MAPIE to their own models, ensuring more reliable and robust predictions.\n\nThis talk targets data scientists, machine learning engineers, and researchers with a basic understanding of machine learning concepts. Familiarity with scikit-learn and other popular machine learning libraries is helpful but not required.\n\nBy the end of the talk, attendees will have gained valuable insights into uncertainty quantification and risk control, as well as hands-on experience using MAPIE to bring uncertainty quantification to their machine learning models.\n\nApproximate time breakdown:\n0-3 minutes: Why perform uncertainty quantification?\n3-15 minutes: Quick dive into the conformal prediction intervals for regression and classification.\n15-20 minutes: Overview of a real-world application + Interactive demo of MAPIE.\n20-30 minutes: Q&A and discussion.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZSXC3H", "name": "Louis Lacombe", "avatar": "https://pretalx.com/media/avatars/ZSXC3H_ihEuyXq.webp", "biography": "Louis Lacombe, Data Scientist at Capgemini Invent, France.\n\nLouis Lacombe, a graduate of the MSc program at the University of Bocconi, joined Quantmetry (now Capgemini Invent) in 2022. He began his journey in conformal predictions with the implementation of conformalized quantile regression in the MAPIE library.\nCurrently, he is one of the core developers of the library and focuses on enhancing its visibility by demonstrating diverse use cases and by integrating conformal predictions into clients' algorithms and advocating for a trusted AI approach.", "public_name": "Louis Lacombe", "guid": "ebce8bb3-d160-5643-be5f-8aeab5db2f23", "url": "https://pretalx.com/pydata-paris-2024/speaker/ZSXC3H/"}, {"code": "RNCPQU", "name": "Thibault Cordier", "avatar": "https://pretalx.com/media/avatars/RNCPQU_sET9YFO.webp", "biography": "Thibault Cordier is a Data and Research Scientist at Capgemini Invent, where he is a member of the Lab Invent team in France and serves as the technical leader of the MAPIE project.\n\nPrior to joining the research team at Capgemini Invent, he earned his PhD in Computer Science in 2023 at Avignon University.\n\nUp to now, his research has focused on distribution-free inference and conformal prediction, with applications in computer vision, natural language processing, and time series analysis.", "public_name": "Thibault Cordier", "guid": "bca5c4af-8e00-5a4c-b957-107b81da4621", "url": "https://pretalx.com/pydata-paris-2024/speaker/RNCPQU/"}, {"code": "CJLPSK", "name": "Valentin Laurent", "avatar": "https://pretalx.com/media/avatars/CJLPSK_uStMCCN.webp", "biography": "Senior Data Scientist @ Capgemini Invent\n\nGetting onboard to lead the team behind MAPIE, an open-source library within the sklearn-contrib ecosystem, focused on conformal predictions.\n\nAfter earning a MSc in Computer Science from \u00c9cole Centrale, I spent a few years in product management before returning to more technical roles.\n\nLet\u2019s connect!", "public_name": "Valentin Laurent", "guid": "aefe8a12-61ca-52ef-8ecf-a1328e8c2ae1", "url": "https://pretalx.com/pydata-paris-2024/speaker/CJLPSK/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/QQSZCJ/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/QQSZCJ/", "attachments": []}, {"guid": "068a2a17-2d0e-57cf-b3a7-7f6189c82985", "code": "DKHKHG", "id": 48161, "logo": null, "date": "2024-09-26T14:25:00+02:00", "start": "14:25", "end": "2024-09-26T14:55:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-48161-adaptive-prediction-intervals", "url": "https://pretalx.com/pydata-paris-2024/talk/DKHKHG/", "title": "Adaptive Prediction Intervals", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Adaptive prediction intervals, which represent prediction uncertainty, are crucial for practitioners involved in decision-making. Having an adaptivity feature is challenging yet essential, as an uncertainty measure must reflect the model's confidence for each observation. Attendees will learn about state-of-the-art algorithms for constructing adaptive prediction intervals, which is an active area of research.", "description": "Proper prediction uncertainty estimation is critical, particularly when data-driven decision-making is in place. Point predictions alone are insufficient as they only represent the conditional expectation of a target variable. Therefore, to make well-informed decisions, one has to quantify the variance of probability distribution associated with a point prediction. Prediction intervals provide this valuable information.\n\nConformal prediction is a powerful distribution-free framework allowing to construct prediction intervals with valid marginal coverage. However, in real-life scenarios, predictive model performance is not homogeneous; there are regions in the input space where it performs better, leading to smaller prediction uncertainties that should be mapped to narrower prediction intervals. This capability to adapt interval lengths based on local performance is called adaptivity.\n\nIn this talk, targeted towards data scientists who develop and deploy predictive models, we review and benchmark algorithms for constructing adaptive prediction intervals. The key takeaway for the audience will be learning about the algorithms for building adaptive prediction intervals and understanding how to apply them in their own work.", "recording_license": "", "do_not_record": false, "persons": [{"code": "3PFPHT", "name": "Andro Sabashvili", "avatar": "https://pretalx.com/media/avatars/3PFPHT_MmyZ4nW.webp", "biography": "Andro Sabashvili transitioned to a data science career after obtaining a PhD in theoretical physics. Andro's data science journey commenced at Bank of Georgia, where he sharpened his skills in developing end-to-end machine learning projects, such as credit risk, propensity, and churn models using advanced machine learning algorithms. After designing a novel algorithm to automate the hyperparameter optimization process, which is currently patent-pending, he joined the startup company Dressler Consulting, where he led a team of data scientists building an automated machine learning platform. In his current role as a Staff Data Scientist at Majid Al Futtaim, Andro is focused on scaling up sales forecasting for thousands of stores, marketing mix modeling, marketing campaign impact estimation, and customer segmentation.", "public_name": "Andro Sabashvili", "guid": "25dd7ac5-99b8-55fc-b1d3-a40b81d08dfc", "url": "https://pretalx.com/pydata-paris-2024/speaker/3PFPHT/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/DKHKHG/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/DKHKHG/", "attachments": []}, {"guid": "ea242344-ba20-548d-bcc6-75efc7803109", "code": "7JJDF8", "id": 48353, "logo": null, "date": "2024-09-26T15:00:00+02:00", "start": "15:00", "end": "2024-09-26T15:30:00+02:00", "duration": "00:30", "room": "Gaston Berger", "slug": "pydata-paris-2024-48353-an-update-on-the-latest-scikit-learn-features", "url": "https://pretalx.com/pydata-paris-2024/talk/7JJDF8/", "title": "An update on the latest scikit-learn features", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "In this talk, we provide an update on the latest `scikit-learn` features that have been implemented in versions 1.4 and 1.5. We will particularly discuss the following features:\n\n- the metadata routing API allowing to pass metadata around estimators;\n- the `TunedThresholdClassifierCV` allowing to tuned operational decision through custom metric;\n- better support for categorical features and missing values;\n- interoperability of array and dataframe.", "description": "In this talk, we present a couple of important novelties available `scikit-learn` since version 1.4.\n\nFirst, we discuss the new metadata routing feature and its API. We start by giving a concrete example of metadata through the usage of `sample_weight` and `groups`. These parameter were previously available in many estimators, metrics, and evaluation tools. However, in old `scikit-learn` version, this feature came with shortcomings that we examine. Then, we show how the new metadata routing API solves these challenges with examples that involve some nested-ness. Concretely, we show how to enable and use this routing on a use-case involving cross-validation, `scikit-learn` pipeline, and grid-search. We further give another concrete example where metadata routing unlock an important feature: it is now possible to optimize a classifier decision threshold using a dedicated business metric. While this was not possible in the past, it is now possible in `scikit-learn` thanks to metadata routing coupled with a new meta-estimator called the `TunedThresholdClassifierCV` that allows optimizing the classification decision threshold.\n\nThen, we present some native handling of categorical and missing data in some estimators (i.e., gradient-boosting and random forest) that leverage `pandas` data types.\n\nFinally, we present some advancements regarding interoperability with different input data. Notably, we show the premise of adopting the so-called Array-API (<https://data-apis.org/array-api/>). This work brings `scikit-learn` to the GPU processing land and allows for improved performance for some algorithms. We showcase an example using a typical machine learning pipeline.  In addition, we present the recent support for another dataframe library, `polars`.", "recording_license": "", "do_not_record": false, "persons": [{"code": "KMDJAL", "name": "Guillaume Lemaitre", "avatar": "https://pretalx.com/media/avatars/KMDJAL_DPeMBoG.webp", "biography": "Guillaume is an open-source software engineer working at :probabl. He is a core maintainer of the scikit-learn and imbalanced-learn libraries.", "public_name": "Guillaume Lemaitre", "guid": "41faba07-5db9-5480-8bd8-f8de3b4d4146", "url": "https://pretalx.com/pydata-paris-2024/speaker/KMDJAL/"}, {"code": "MUW3K7", "name": "Stefanie Senger", "avatar": "https://pretalx.com/media/avatars/MUW3K7_SKMvPrH.webp", "biography": "Stefanie is an open source developer at :probabl. and a contributor to scikit-learn.", "public_name": "Stefanie Senger", "guid": "f349211c-4c2d-5736-b9eb-30353418d1c3", "url": "https://pretalx.com/pydata-paris-2024/speaker/MUW3K7/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/7JJDF8/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/7JJDF8/", "attachments": []}, {"guid": "57f99def-2496-57f4-bf09-169806c89fb4", "code": "7VYUGP", "id": 48697, "logo": null, "date": "2024-09-26T15:45:00+02:00", "start": "15:45", "end": "2024-09-26T16:30:00+02:00", "duration": "00:45", "room": "Gaston Berger", "slug": "pydata-paris-2024-48697-keynote-handling-predictive-uncertainty-in-machine-learning", "url": "https://pretalx.com/pydata-paris-2024/talk/7VYUGP/", "title": "Keynote: Handling predictive uncertainty in Machine Learning", "subtitle": "", "track": null, "type": "Keynote", "language": "en", "abstract": "Machine Learning practitioners build predictive models from \"noisy\" data resulting in uncertain predictions. But what does \"noise\" mean in a machine learning context?", "description": "Over years of developing scikit-learn and exchanging with both applied data scientists and academics, I have progressively refined my understanding on topics such as the fundamental sources of \"noisy\" data, how to evaluate and improve the design of predictive models with respect to ranking power and probabilistic calibration, how to handle predictive uncertainty and turn probabilistic forecasts into optimal decisions with respect to an application-specific utility function.\n\nThe goal of this keynote to share what I learned with you and hopefully help you reflect on how to quantify the value of predictions and better use scikit-learn and other predictive modeling tools.\n\nSlides: https://docs.google.com/presentation/d/1EBCSCDQ3nTPaKZGx9ZLWXfvkD1Y-ODo9j_ETAnx5zLQ/edit?usp=sharing", "recording_license": "", "do_not_record": false, "persons": [{"code": "NEUMLP", "name": "Olivier Grisel", "avatar": "https://pretalx.com/media/avatars/NEUMLP_RirLVrG.webp", "biography": "Olivier is an Open Source Fellow at probabl and a core contributor to the scikit-learn machine learning library.", "public_name": "Olivier Grisel", "guid": "20888933-e3d6-5580-8ff2-1b072174f0f2", "url": "https://pretalx.com/pydata-paris-2024/speaker/NEUMLP/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/7VYUGP/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/7VYUGP/", "attachments": []}], "Louis Armand 1 - Est": [{"guid": "461deee0-001a-55b0-b4f9-2db4896713a6", "code": "A7VSKN", "id": 48537, "logo": null, "date": "2024-09-26T09:45:00+02:00", "start": "09:45", "end": "2024-09-26T10:15:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48537-fast-networkx-and-how-accelerated-backends-are-changing-graph-analytics", "url": "https://pretalx.com/pydata-paris-2024/talk/A7VSKN/", "title": "Fast NetworkX and How Accelerated Backends Are Changing Graph Analytics", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "NetworkX is arguably the most popular graph analytics library available today, but one of its greatest strengths - the pure-python implementation - is also possibly its biggest weakness.  If you're a seasoned data scientists or a new student of the fascinating field of graph analytics, you're probably familiar with NetworkX and interested in how to make this extremely easy-to-use library powerful enough to handle realistically large graph workflows that often exceed the limitations of its pure-python implementation.\n\nThis talk will describe a relatively new capability of NetworkX; support for accelerated backends, and how they can benefit NetworkX users by allowing it to finally be both easy to use and fast. Through the use of backends, NetworkX can also be incorporated into workflows that take advantage of similar accelerators, such as Accelerated Pandas (cudf.pandas), to finally make these easy to use solutions scale to larger problems.\n\nAttend this talk to learn about how you can leverage the various backends available to NetworkX today to seamlessly run graph analytics on GPUs, use GraphBLAS implementations, and more, all without leaving the comfort and convenience of the most popular graph analytics library available.", "description": "NetworkX is arguably the most popular graph analytics library available today; it's extremely easy to install and use, supports a huge assortment of popular analytics, and has excellent support via its community and documentation, and the number of PyPI downloads - typically around 47 million every month - support this.  But despite these benefits, many users are forced to move on to another graph analytics library when the limitations of NetworkX's pure-python implementation prevents them from using it for larger workloads.\n\nWill NetworkX users ever be able to have the ubiquity and ease-of-use they've grown accustomed to without the limitations of a pure-python implementation?\n\nThe answer is yes!  Graph analytics that are fast and scalable but still easy to use and well-documented are possible simply by using NetworkX with an accelerated backend.  The backends available to NetworkX today enable support for running analytics on GPUs, leveraging linear algebra through GraphBLAS, and more, all without requiring users to change their existing NetworkX code.  This changes the state of graph analytics by making it accessible to more users and more use cases than ever before.\n\nThis talk will describe how you can take advantage of the several NetworkX backends available today as well as how you can incorporate your own custom backend to handle graph-related problems that simply weren't feasible before. Examples, benchmark comparisons, and descriptions of the underlying dispatching mechanism that makes alternate backends possible will be provided to illustrate how ease of use and performance can finally coexist in NetworkX.  This talk will also show how NetworkX with backend acceleration can be incorporated into workflows that take advantage of similar accelerators, such as Accelerated Pandas (cudf.pandas), to finally make these easy to use solutions scale to larger problems.  Attend this talk to hear just how easy it is to use NetworkX to unlock the hidden secrets in your large, real-world data that only fast graph analytics can reveal.  Detailed background knowledge of NetworkX and graph analytics is not required.\n\nAn approximate time breakdown is as follows:\n5 mins - Brief introduction to graph analytics and NetworkX, why it's the most popular graph analytics library, and what its limitations are.\n15 mins - How do accelerated backends work, what's available, and how can they make NetworkX work for workloads both big and small?\n5 mins - NetworkX's bright future, and how you can help!\n5 mins - Q&A", "recording_license": "", "do_not_record": false, "persons": [{"code": "KZFC9U", "name": "Erik Welch", "avatar": "https://pretalx.com/media/avatars/KZFC9U_SNwAeLd.webp", "biography": "Erik Welch is a senior system software engineer on the RAPIDS cuGraph team at NVIDIA and a core NetworkX developer. He has 20 years' experience using Python as a scientist, engineer, and open-source developer on a wide range of data and high-performance computing problems. He primarily works on `nx-cugraph`, an accelerated backend to NetworkX, and is the primary maintainer of the popular `toolz` library.", "public_name": "Erik Welch", "guid": "0aa50d9a-6b49-57df-9d35-aa8678d5164e", "url": "https://pretalx.com/pydata-paris-2024/speaker/KZFC9U/"}, {"code": "YQRY7H", "name": "Rick Ratzel", "avatar": null, "biography": "Rick Ratzel is a member of the RAPIDS team at NVIDIA, working on cuGraph - a library of GPU-accelerated graph algorithms. Rick joined NVIDIA in January 2019, bringing several years of experience as a technical lead for teams in industries that include test and measurement, electronic design automation, and scientific computing.", "public_name": "Rick Ratzel", "guid": "306b26c8-d76e-55fb-999d-866d49b2705c", "url": "https://pretalx.com/pydata-paris-2024/speaker/YQRY7H/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/A7VSKN/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/A7VSKN/", "attachments": []}, {"guid": "469981bf-2074-518a-841e-bcbe9da04b78", "code": "E7LHZX", "id": 48386, "logo": null, "date": "2024-09-26T10:30:00+02:00", "start": "10:30", "end": "2024-09-26T11:00:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48386-foundational-models-for-time-series-forecasting-are-we-there-yet", "url": "https://pretalx.com/pydata-paris-2024/talk/E7LHZX/", "title": "Foundational Models for Time Series Forecasting: are we there yet?", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Transformers are everywhere: NLP, Computer Vision, sound generation and even protein-folding. Why not in forecasting? After all, what ChatGPT does is predicting the next word. Why this architecture isn't state-of-the-art in the time series domain?\n\nIn this talk, you will understand **how Amazon Chronos and Salesforece's Moirai transformer-based forecasting models work**, the **datasets used to train them** and **how to evaluate them** to see if they are a good fit for your use-case.", "description": "This is a pragmatic talk for Generative AI and ML practitioners alike, eager to understand how researchers managed to **adapt transformer-based architectures to forecasting problems**, as well as how to **evaluate new models for specific use-cases**.\n\nThe first part of the talk explains how researchers **discretised time series into a finite \"dictionary\"** and the **theoretical limitations** of this approach, such as how to deal with different sampling frequencies during training.\n\nWe will then make a parallel with Large Language Models (LLMs) scaling laws to evaluate the data strategies used to train such universal forecasting models. In other words, we will ask the question **whether we have enough publicly available time-series data to train foundational models**, and how this can **affect such models' evaluation**.\n\nFinally, we will display how we attempted to **benchmark those models against a robust baseline of models**, and where our experiment is compared to the publicly available results.\n\nNo specific prior knowledge is required for attendance. Though forecasting practitioners stand to gain the most from this talk, every practitioner in machine learning or generative AI can follow along and draw the key conclusions.\n\n**Outline**\nMinutes 1-3. Problem statement: challenges in adapting transformers to the time-series domain.\nMinutes 3-10. How Chronos and Moirai are implemented.\nMinutes 10-15. Do we have enough data to train universal forecasters?\nMinutes 15-25. Lessons learnt from evaluating transformer-based models for a specific use-case.\nMinutes 25-30. Wrap up and Q&A.", "recording_license": "", "do_not_record": false, "persons": [{"code": "LCEK33", "name": "Luca Baggi", "avatar": "https://pretalx.com/media/avatars/LCEK33_5eAzatc.webp", "biography": "ML Engineer @xtream", "public_name": "Luca Baggi", "guid": "67583db7-da6f-5b51-844f-9cf951b9a0a6", "url": "https://pretalx.com/pydata-paris-2024/speaker/LCEK33/"}, {"code": "VZULQF", "name": "Gabriele Orlandi", "avatar": "https://pretalx.com/media/avatars/VZULQF_1XrmE48.webp", "biography": "Data Scientist @ xtream", "public_name": "Gabriele Orlandi", "guid": "bb20fdf1-71ca-5e2f-a5de-3e947f57eb3c", "url": "https://pretalx.com/pydata-paris-2024/speaker/VZULQF/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/E7LHZX/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/E7LHZX/", "attachments": []}, {"guid": "58ba036d-d3b3-5a61-b4cc-199f0d0657a3", "code": "KZ8ZPA", "id": 48521, "logo": null, "date": "2024-09-26T11:05:00+02:00", "start": "11:05", "end": "2024-09-26T11:35:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48521-sktime-python-toolbox-for-time-series-next-generation-ai-deep-learning-and-foundation-models", "url": "https://pretalx.com/pydata-paris-2024/talk/KZ8ZPA/", "title": "sktime - python toolbox for time series: next-generation AI \u2013 deep learning and foundation models", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "sktime is a widely used scikit-learn compatible library for learning with time series. sktime is easily extensible by anyone, and interoperable with the pydata/numfocus stack. \n\nThis talk presents progress, challenges, and newest features off the press, in extending the sktime framework to deep learning and foundation models.\n\nRecent progress in generative AI and deep learning is leading to an ever-exploding number of popular \u201cnext generation AI\u201d models for time series tasks like forecasting, classification, segmentation.\n\nParticular challenges of the new AI ecosystem are inconsistent formal interfaces, different deep learning backends, vendor specific APIs and architectures which do not match sklearn-like patterns well \u2013 every practitioner who has tried to use at least two such models at the same time (outside sktime) will have their individual painful memories.\n\nWe show how sktime brings its unified interface architecture for time series modelling to the brave new AI frontier, using novel design patterns building on ideas from hugging face and scikit-learn, to provide modular, extensible building blocks with a simple specification language.", "description": "sktime\u2019s technical mission is to integrate the ecosystem of time series modelling behind a simple, unified, scikit-learn like first-order modelling language. After a years-long journey of integration and stringent architecture, sktime provides unified interfaces for hundreds of algorithms for tasks such as time series forecasting or classification, which can be used as building blocks in a highly composable interface.\n\nsktime compatible algorithms \u2013 1st party in the package itself, or 3rd party in private extensions \u2013 follow consistent, exchangeable interfaces, which are kept robust and extensible through strong API checks and testing tools.\n\nWhile sktime, as a framework and marketplace, is nowadays a go-to place for the \u201ccanon\u201d of statistical, econometric, ML models, a new, rapidly evolving \u201cwild west\u201d has emerged, with a growing wave of time series foundation models being released, and renewed interest in deep learning based algorithms.\n\nIn the talk, we will discuss specific challenges that need to be addressed, on the framework layer, estimator specific dependency management layer, related to availability of algorithms, and project governance:\n-\tThe need to cover pre-training, fine-tuning, which implies handling of pre-trained weights - e.g., from hugging face - as well as extending sklearn-like interfaces to fine-tuning style update or extending task models such as to global forecasting\n-\tThe need to cover back-end abstractions for data containers, deep learning frameworks, and possibly vendor specific APIs; implying interfaces and layers usually not found in scikit-learn-like libraries; and patterns that require closer integration across packages\n-\tAs an explicitly neutral, non-commercial library, dealing with extreme commercial pressures and related behavioural patterns, e.g., companies wanting to establish \u201ctheir algorithm\u201d and trying to nudge users towards \u2013 and lock them in to \u2013 proprietary APIs rather than framework APIs\n-\tThe interaction with open research and data ecosystems, as foundation models require closer integration with data corpora for training and tuning\n\nWe think the above pose a new set fundamental challenges, similar to the initial gap that sktime had to bridge, between a world without a comprehensive framework for time series ML, and the current state in which sktime exists (and in which multiple projects take inspiration from its design ideas).\n\nOur talk outlines new features, current roadmap, and partial progress towards building a framework in which next generation algorithms do not only coexist with classical ML, but become fully interoperable, comparable, and composable.\n\nsktime is developed by an open community, with aims of ecosystem integration in a commercially neutral, charitable space. We welcome contributions or donations, and seek to provides opportunity for anyone worldwide.", "recording_license": "", "do_not_record": false, "persons": [{"code": "KGV7MZ", "name": "Franz Kiraly", "avatar": null, "biography": null, "public_name": "Franz Kiraly", "guid": "36e477dd-5418-53b4-8483-b2759f000839", "url": "https://pretalx.com/pydata-paris-2024/speaker/KGV7MZ/"}, {"code": "GB89CS", "name": "Benedikt Heidrich", "avatar": "https://pretalx.com/media/avatars/GB89CS_ez5rvaa.webp", "biography": "I completed my PhD in deep learning based time series forecasting in 2023 with the Karlsruhe Institute of Technology. In sktime, I am focusing on forecasting methods (mainly deep learning based ones) and implementing pipelines.", "public_name": "Benedikt Heidrich", "guid": "c0bd7767-17b1-5074-bd19-c20c1d3e4ad6", "url": "https://pretalx.com/pydata-paris-2024/speaker/GB89CS/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/KZ8ZPA/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/KZ8ZPA/", "attachments": []}, {"guid": "f421759b-c93c-5ab6-8791-3e969ec4e031", "code": "AN7PX3", "id": 48504, "logo": null, "date": "2024-09-26T11:40:00+02:00", "start": "11:40", "end": "2024-09-26T12:10:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48504-chainsail-facilitating-sampling-of-multimodal-probability-distributions", "url": "https://pretalx.com/pydata-paris-2024/talk/AN7PX3/", "title": "Chainsail: facilitating sampling of multimodal probability distributions", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Markov chain Monte Carlo (MCMC) methods, a class of iterative algorithms that allow sampling almost arbitrary probability distributions, have become increasingly popular and accessible to statisticians and scientists. But they run into difficulties when applied to multimodal probability distributions. These occur, for example, in Bayesian data analysis, when multiple regions in the parameter space explain the data equally well or when some parameters are redundant. Inaccurate sampling then results in incomplete and misleading parameter estimates.\nMarkov chain Monte Carlo (MCMC) methods, a very popular class of iterative algorithms that allow sampling almost arbitrary probability distributions, run into difficulties when applied to multimodal probability distributions. These occur, for example, in Bayesian data analysis, when multiple regions in the parameter space explain the data equally well or when some parameters are redundant.\nIn this talk, intended for data scientists and statisticians with basic knowledge of MCMC and probabilistic programming, I present Chainsail, an open-source web service written entirely in Python. It implements Replica Exchange, an advanced MCMC method designed specifically to improve sampling of multimodal distributions.\nChainsail makes this algorithm easily accessible to users of probabilistic programming libraries by automatically tuning important parameters and exploiting easy on-demand provisioning of the (increased) computing resources necessary for running Replica Exchange.", "description": "The presentation starts with a small refresher on MCMC and an introduction to the Replica Exchange algorithm. A second part will focus on the web service architecture, and finally, I will show examples of how Chainsail improves MCMC sampling.\nThe audience will learn about a common pitfall when doing Bayesian data analysis and how an algorithm originally developed in the field of solid state physics can help. Furthermore, the architecture part of the presentation shows an example of how to set up a complex, multi-component web service using Kubernetes and other popular software.", "recording_license": "", "do_not_record": false, "persons": [{"code": "S8HE7K", "name": "Simeon Carstens", "avatar": "https://pretalx.com/media/avatars/S8HE7K_qX8iz4z.webp", "biography": "I'm a data / software engineer at Modus Create, working on projects in GenAI and biotech domains. I have a background in physics and computational structural biology.", "public_name": "Simeon Carstens", "guid": "71b0d255-f35a-54d8-9e6e-57293fd54f42", "url": "https://pretalx.com/pydata-paris-2024/speaker/S8HE7K/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/AN7PX3/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/AN7PX3/", "attachments": []}, {"guid": "65846d7e-c33a-58bb-bc33-40efc525e7a6", "code": "YVFJFF", "id": 48494, "logo": null, "date": "2024-09-26T13:50:00+02:00", "start": "13:50", "end": "2024-09-26T14:20:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48494-open-source-sustainability-philanthropy-building-contributor-communities", "url": "https://pretalx.com/pydata-paris-2024/talk/YVFJFF/", "title": "Open Source Sustainability & Philanthropy: Building Contributor Communities", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Open Source Software, the backbone of today\u2019s digital infrastructure, must be sustainable for the long-term. Qureshi and Fang (2011) find that motivating, engaging, and retaining new contributors is what makes open source projects sustainable.\n\nYet, as Steinmacher, et al. (2015) identifies, first-time open source contributors often lack timely answers to questions, newcomer orientation, mentors, and clear documentation. Moreover, since the term was first coined in 1998, open source lags far behind other technical domains in participant diversity. Trinkenreich, et al. (2022) reports that only about 5% of projects were reported to have women as core developers, and women authored less than 5% of pull requests, but had similar or even higher rates of pull request acceptances to men. So, how can we achieve more diversity in open source communities and projects?\n\nBloomberg\u2019s Women in Technology (BWIT) community, Open Source Program Office (OSPO), and Corporate Philanthropy team collaborated with NumFOCUS to develop a volunteer incentive model that aligns business value, philanthropic impact, and individual technical growth. Through it, participating Bloomberg engineers were given the opportunity to convert their hours spent contributing to the pandas open source project into a charitable donation to a non-profit of their choice.\n\nThe presenters will discuss how we wove together differing viewpoints: non-profit foundation and for-profit corporation, corporate philanthropy and engineers, first-time contributors and core devs. They will showcase why and how we converted technical contributions into charitable dollars, the difference this community-building model had in terms of creating a diverse and sustained group of new open source contributors, and the viability of extending this to other open source projects and corporate partners to contribute to the long-term sustainability of open source\u2014thereby demonstrating the true convergence of tech and social impact.\n\nNOTE: \n[1] Qureshi, I, and Fang, Y. \"Socialization in open source software projects: A growth mixture modeling approach.\" 2011.\n[2] Steinmacher, I., et al. \"Social barriers faced by newcomers placing their first contribution in open source software projects.\" 2015.\n[3] Trinkenreich, B., et al. \"Women\u2019s participation in open source software: A survey of the literature.\" 2022.", "description": "Built on collaborative communities, open source is critical infrastructure on which our digital world depends. It's important that me+we are represented in the communities sustaining open source. \n\nBloomberg joined forces with the NumFOCUS Foundation in 2023 to pilot a new approach to open source sustainability to address the lack of diversity in\u2014and drop-off rate of\u2014new open source contributors to the pandas project. The result is a framework for sustainable open source contribution that open source projects can scale for growth and companies can scale for business and philanthropic value. This new volunteer incentive model leads the way in breaking down boundaries between philanthropy and technology.\n\nThis series is leading the way in breaking down boundaries between philanthropic and technology alignment and impact for public good. This informative talk describes the structure of the series and how it helps to reimagine both the definition of philanthropy and how D&I leaders can raise the bar for technical excellence.\n\n\n1. Welcome and initial remarks (5 mins)\n   a. The importance of open source to Bloomberg\n   b. Intro to Bloomberg\u2019s pandas Sustainability Series\n2. How Bloomberg sustains Open Source though philanthropy (5 mins)\n   a. Bloomberg\u2019s Open Source Program Office (OSPO) and its Sustainability pillar\n   b. Intro to the firm\u2019s Open Source Dollars For Your Hours program\n          i. Turning hours spent making open source contributions into charitable donations to the non-profit of engineers\u2019 choice\n3. Impact of the Bloomberg Women in Technology (BWIT) community (5 mins)\n    a. Intro to BWIT Philanthropy\n    b. BWIT\u2019s partnership with OSPO to build collaborative communities in the open source ecosystem\n    c. Reimagining the definition of philanthropy and demonstrating how women leaders in engineering continue to raise the bar for technical excellence\n4. Pandas Sustainability Series (5 min)\n   a. How we made it happen\n   b. High notes\n   c. What we learned\n5. Create your own sustainability series (5 mins)\n   a. How you can do it too\n6. Audience Q&A (5 mins)", "recording_license": "", "do_not_record": false, "persons": [{"code": "WNWQ9A", "name": "Alyssa Wright", "avatar": "https://pretalx.com/media/avatars/WNWQ9A_cmzdo08.webp", "biography": "Alyssa helps lead Bloomberg\u2019s Open Source Program Office (OSPO) in the Office of the CTO. Bloomberg's OSPO serves as the center of excellence for Bloomberg\u2019s consumption of open source software and engagement with the broader open source community. When not helping define open source strategies and implementing partnerships and best practices, she is typically cycling to a mountaintop... or sleeping after having done so.", "public_name": "Alyssa Wright", "guid": "759f707a-7a27-5f77-adb1-8f20bb8e54d2", "url": "https://pretalx.com/pydata-paris-2024/speaker/WNWQ9A/"}, {"code": "L8KCQM", "name": "Devpriya Dave", "avatar": "https://pretalx.com/media/avatars/L8KCQM_kYebIJE.webp", "biography": "Devpriya Dave is a software engineer on the FX Options team at Bloomberg. While at Georgia Tech obtaining her master's degree, she helped design and build the system behind Georgia Tech's Machine Learning for Trading online course (https://www.udacity.com/course/machine-learning-for-trading--ud501). She is passionate about STEM mentorship and is always looking for ways to give back. In her free time, she enjoys reading books with a cup of coffee.", "public_name": "Devpriya Dave", "guid": "f139bc8a-3e77-5e35-ac93-981dba69a1c0", "url": "https://pretalx.com/pydata-paris-2024/speaker/L8KCQM/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/YVFJFF/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/YVFJFF/", "attachments": []}, {"guid": "e3fe5aaf-6074-59a4-959e-38c8046a9ec7", "code": "QMZRTS", "id": 48432, "logo": null, "date": "2024-09-26T14:25:00+02:00", "start": "14:25", "end": "2024-09-26T14:55:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48432-counting-down-for-cra-updates-and-expectations", "url": "https://pretalx.com/pydata-paris-2024/talk/QMZRTS/", "title": "Counting down for CRA - updates and expectations", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "The EU Commission is likely to vote on the Cyber Resilience Act (CRA) later this year. In this talk we will look at the timeline for the new legislation, any critical discussions happening around implementation and most importantly, the new responsibilities outlined by the CRA. We\u2019ll also discuss what the PSF is doing for CPython and for PyPI and what each of us in the Python ecosystem might want to do to get ready for a new era of increased certainty \u2013 and liability \u2013 around security.", "description": "The EU Commission is likely to vote on the Cyber Resilience Act (CRA) later this year. The CRA is an ambitious step towards protecting consumers from software security issues by creating a new list of responsibilities for software developers and providers. The Act also creates a new category of actor known as an \u201cOpen Source Steward\u201d which we think makes important allowances for public open source repositories like CPython and Python Package Index (PyPI.) Once the dust settles, everyone who makes software will need to consider the CRA\u2019s mandates in their security roadmaps. \n\nIn this talk we will look at the timeline for the new legislation, any critical discussions happening around implementation and most importantly, the new responsibilities outlined by the CRA. We\u2019ll also discuss what the PSF is doing for CPython and for PyPI and what each of us in the Python ecosystem might want to do to get ready for a new era of increased certainty \u2013 and liability \u2013 around security. \n\n## Target audience\n\nDevelopers and maintainers whose project or product may be affected by the CRA. European legislation won\u2019t just affect the European market, it will affect the software industry and the open source community globally as it is very hard to segregate one project or product from the EU market. So, this is for everyone in the Python community who shares their code with the world. \n\n## Goal\n\nTo educate the general public about CRA - how it can affect us and how to get ready for it. We also want to provide more information for the Python community about what has been done by the PSF regarding the CRA to reassure them that the Python community is aware and getting prepared for the CRA.\n\n## Outline\n\n- Introduction\n  - what is CRA\n  - Highlight of the CRA text as of now\n  - How we may be affected\n- Getting ready\n  - What has PSF done for cyber security\n  - What has PSF done for CRA specifically\n  - What is PSF planning to do to support the community\n- Call for action\n  - How to be prepared as an individual\n  - How to be prepared if you are maintaining a project\n  - How to support PSF to be ready for CRA", "recording_license": "", "do_not_record": false, "persons": [{"code": "8EGVC9", "name": "Cheuk Ting Ho", "avatar": "https://pretalx.com/media/avatars/8EGVC9_LbezfQb.webp", "biography": "After having a career in Data Scientist and Developer Relations, Cheuk dedicated her work to the open-source community and founded [CMD Limes](https://cmdlimes.com/), a Python consultants cooperation. She has also co-founded [Humble Data](https://humbledata.org/), a beginner Python workshop that has been happening around the world. She has served the EuroPython Society board for two years and is now a fellow and director of the Python Software Foundation.", "public_name": "Cheuk Ting Ho", "guid": "6acb0b45-07a8-5f1c-a3fa-45ae8f0a9858", "url": "https://pretalx.com/pydata-paris-2024/speaker/8EGVC9/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/QMZRTS/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/QMZRTS/", "attachments": []}, {"guid": "2cc420f4-37a8-5652-97a3-171376188589", "code": "TK9TKL", "id": 48184, "logo": null, "date": "2024-09-26T15:00:00+02:00", "start": "15:00", "end": "2024-09-26T15:30:00+02:00", "duration": "00:30", "room": "Louis Armand 1 - Est", "slug": "pydata-paris-2024-48184-track-your-code-s-c02-emissions-with-code-carbon", "url": "https://pretalx.com/pydata-paris-2024/talk/TK9TKL/", "title": "Track your code's C02 emissions with Code Carbon", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Rising concerns over IT's carbon footprint necessitate tools that gauge and mitigate these impacts. This session introduces CodeCarbon, an open-source tool that estimates computing's carbon emissions by measuring energy use across hardware components. Aimed at AI researchers and data scientists, CodeCarbon provides actionable insights into the environmental costs of computational projects, supporting efforts towards sustainability without requiring deep technical expertise.\n\nThis talk from the main contributors of Code Carbon will cover the environmental impact of IT, the possibilities to estimate it and a demo of CodeCarbon.", "description": "**Background**: The IT sector's environmental impact is significant and growing, driven by the surge in data processing and computational demands, particularly within the realms of AI and data science. Addressing this requires tools that not only measure but also help manage and reduce these impacts.\n\n**Introducing CodeCarbon**: CodeCarbon is a Python package that offers a straightforward method for estimating the carbon emissions of computing tasks. It calculates energy consumption (GPU, CPU, RAM) and translates this into carbon emissions based on the regional carbon intensity where the computation occurs. It's designed for both technical and non-technical users, with a command line interface and easy integration into existing code bases.\n\n**Session Objectives**:\n\n- *Environmental Impact Awareness*: Highlight the urgency of acknowledging and acting on the carbon footprint associated with IT projects.\n- *Tool Demonstration*: Walk through CodeCarbon's features, including setup, integration into different environments (command line, Jupyter notebook, and direct code integration), and how to utilize the dashboard for visualizing emissions data.\n- *Broad Applicability*: Emphasize CodeCarbon's utility beyond AI and data science, demonstrating its relevance for any computational work seeking to minimize environmental impact.\n- *Community Engagement* : Discuss how developers and researchers can contribute to CodeCarbon, enhancing its accuracy, usability, and impact.\n- *Takeaway* : Participants will gain a thorough understanding of how to leverage CodeCarbon to make informed decisions about the environmental implications of their work. \n\nThe session underscores the importance of measurement in environmental sustainability, echoing Niels Bohr: \"Nothing exists until it is measured.\" By quantifying the carbon footprint of computational tasks, CodeCarbon empowers the community to drive change towards more sustainable practices in technology.\n\nThis talk aims to bridge the gap between awareness and action, providing the PyData community with the tools to actively reduce the carbon footprint of their computational endeavors.", "recording_license": "", "do_not_record": false, "persons": [{"code": "KFGKLG", "name": "Luis Blanche", "avatar": "https://pretalx.com/media/avatars/KFGKLG_nOLrybN.webp", "biography": "I am a freelance Machine Learning Engineer \nI have applied data science and machine learning in various industries. I have grown to enjoy taking things from idea to production and I now have a strong grip on most of the satellite fields of data science (data engineering, devops/mlops, unit testing, containers, git and CI/CD, cloud, product development).\nI am mostly interested in work that have a neutral if not positive impact on climate change and biodiversity. I spare some time off missions so I can dedicate it to open source project like Code Carbon", "public_name": "Luis Blanche", "guid": "126584aa-3f18-550c-8fb1-26bd86597551", "url": "https://pretalx.com/pydata-paris-2024/speaker/KFGKLG/"}, {"code": "ZJSPA3", "name": "Beno\u00eet Courty", "avatar": "https://pretalx.com/media/avatars/ZJSPA3_7AQfo3h.webp", "biography": "Beno\u00eet Courty is a data scientist with over 20 years of experience in the tech industry. He began his career as an inside subcontractor at a large French company, where he worked on a variety of projects. In 2015, he co-founded a UAV startup that developed technology to automatically fly around buildings to detect cracks. He then worked as a freelance data scientist for banking and TV companies. Three years ago, he became an internal data scientist at the French National Assembly. He is also a member of Data For Good France, where he discovered CodeCarbon in 2020 and became its main contributor.", "public_name": "Beno\u00eet Courty", "guid": "c00bfe89-56e2-519d-a133-7b1ce8c5fa82", "url": "https://pretalx.com/pydata-paris-2024/speaker/ZJSPA3/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/TK9TKL/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/TK9TKL/", "attachments": []}], "Louis Armand 2 - Ouest": [{"guid": "45594755-0602-50bf-9eca-5857b256abd7", "code": "SNLQWH", "id": 48230, "logo": null, "date": "2024-09-26T09:45:00+02:00", "start": "09:45", "end": "2024-09-26T10:15:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-48230-color-composite-images-from-the-james-webb-space-telescope", "url": "https://pretalx.com/pydata-paris-2024/talk/SNLQWH/", "title": "Color-composite images from the James Webb Space Telescope", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "The astronomical community has built a good amount of software to visualize and analyze the images obtained with the James Webb Space Telescope (JWST). In this talk, I will present the open-source Python package Jdaviz. I will show you how to visualize publicly available JWST images and build the pretty color images that we have all seen in the media. Half the talk will be an introduction to JWST and Jdaviz and half will be a hands on session on a cloud platform (you will only need to create an account) or on your own machine (the package is available on PyPI).", "description": "Do you like the pretty images in those press releases from the James Webb Space Telescope (JWST)? Would you like to learn how to make one yourself using open source software? Yes? Then please come to my talk with your laptop and I will show you how to do it!\n\t\t\nJWST has been observing the Universe for about 2 years now, sending us spectacular images of planets, nebulae, stars, and galaxies. These images are taken in filters at different wavelengths and they need to be combined to produce the stunning color images we see in the press releases. There is various software available to do this. In this talk, I will present an open-source Python package called Jdaviz (J-D-A-viz) which stands for JWST Data Analysis and Visualization. Jdaviz works in the Jupyter environment and the users can interact with it using the interface or using Python commands from a Jupyter Notebook.\n\t\t\nNo need to be a scientist or astronomer to follow this presentation. If you are familiar with Jupyter Lab, the concept of taking images through filters, and the concept of scaling the range and contrast of an image, you will enjoy the ride. The presentation will be light weighted and half of it will be a hands-on session.\n\nIf you like the idea, I will be happy to use 30 minutes of PyData to:\n- give an introduction to JWST and astronomical images; 5 minutes\n- give an introduction to the Python open-source package Jdaviz; 5 minutes\n- be your guide to make a color-composite image using JWST data on a cloud platform (you will need to create an account) or on your own machine (the package is available on PyPI); 15 minutes\n- answer any questions you might have; 5 minutes", "recording_license": "", "do_not_record": false, "persons": [{"code": "DKAQQN", "name": "Camilla Pacifici", "avatar": "https://pretalx.com/media/avatars/DKAQQN_hXnSViL.webp", "biography": "I am an astronomical data scientist at the Space Telescope Science Institute in Baltimore. I work with a team on software engineers to develop a suite of open source tools to visualize and analyze data from the James Webb Space Telescope. I am especially interested in bringing these tools to the wider community and non-scientists who wonder about the Universe.", "public_name": "Camilla Pacifici", "guid": "06a4c129-b9c4-5f96-9f0a-3d95eb691ea6", "url": "https://pretalx.com/pydata-paris-2024/speaker/DKAQQN/"}, {"code": "LPBLCC", "name": "Jesse Averbukh", "avatar": "https://pretalx.com/media/avatars/LPBLCC_M8JXs6G.webp", "biography": "Software engineer on the team that develops Jdaviz.", "public_name": "Jesse Averbukh", "guid": "f4da88ef-7a95-575e-acef-03661c0a5cb5", "url": "https://pretalx.com/pydata-paris-2024/speaker/LPBLCC/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/SNLQWH/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/SNLQWH/", "attachments": []}, {"guid": "7cd9dd92-c5a6-5c41-8f1b-5e87741e188e", "code": "CVA9XR", "id": 48587, "logo": null, "date": "2024-09-26T10:30:00+02:00", "start": "10:30", "end": "2024-09-26T11:00:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-48587-visualization-of-the-sky-in-notebooks-the-ipyaladin-widget-extension", "url": "https://pretalx.com/pydata-paris-2024/talk/CVA9XR/", "title": "Visualization of the sky in Notebooks: the ipyaladin widget extension", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "[Aladin](https://aladin.cds.unistra.fr/aladin.gml) allows to visualize images of the sky or planetary surfaces just as an astronomical \"openstreetmap\" app. The view can be panned and explored interactively. In the [ipyaladin widget](https://github.com/cds-astro/ipyaladin) -- that brings Aladin in the Jupyter Notebook environnement -- these abilities are extended with a python API. The users can send astronomical data in standard formats back and forth the viewer and their Python code. Such data can be images of the sky in different wavelengths, but also tabular data, complex shapes that characterize telescope observation regions, or even special sky features (such as probability region for the provenance of a gravitational event).\n\nWith these already existing features, and current work we are doing with the new development framework `anywidget`, `ipyaladin` is really close to a version 1.0.0. It is already used in its beta version in different experimental science platforms, for example in the ESCAPE [European Science Cluster of Astronomy & Particle Physics](https://projectescape.eu/) project and in the experimental [SKA](https://www.skao.int/en/science-users) (Square Kilometre Array, a telescope for radio astronomy) analysis platform.\n\nIn this presentation, we will share our feedback on the development of a widget thanks to `anywidget` compared to the bare `ipywidget` framework. And we will demonstrate the functionalities of the widget through scientific use cases.", "description": "We write in the abstract that `ipyaladin` is close to its version 1.0.0, but our aim would be to present this release for the PyData event in September.\n- audience: scientific public of amateur astronomers / widget developers\n- background knowledge: none\n- outline: \n  1. problematic of visualizing spherical surfaces\n  2. what we want to show are huge datasets (windowing of the printed data in the field of view)\n  3. how to provide a Python API for this viewer for the scientific community\n  4. the widget development process\n  5. (bonus: beautiful images of the sky)", "recording_license": "", "do_not_record": false, "persons": [{"code": "VTAXCF", "name": "Matthieu Baumann", "avatar": "https://pretalx.com/media/avatars/VTAXCF_bKDcVNx.webp", "biography": "Software rust/javascript developer working at the Strasbourg astronomical data center on the Aladin Lite web visualizer.", "public_name": "Matthieu Baumann", "guid": "7b431d2c-88c3-5c83-a88e-1d893a957883", "url": "https://pretalx.com/pydata-paris-2024/speaker/VTAXCF/"}, {"code": "EKZUUT", "name": "Manon Marchand", "avatar": "https://pretalx.com/media/avatars/EKZUUT_B6qUJRe.webp", "biography": "Pythonista and software engineer at Strasbourg Astronomical Data Centre.", "public_name": "Manon Marchand", "guid": "4260eae4-fe78-51fb-9668-0dd03873bee8", "url": "https://pretalx.com/pydata-paris-2024/speaker/EKZUUT/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/CVA9XR/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/CVA9XR/", "attachments": []}, {"guid": "9e9dc58f-d29b-50f8-8f36-8ee0190675e8", "code": "WTDVCC", "id": 47030, "logo": "https://pretalx.com/media/pydata-paris-2024/submissions/WTDVCC/DataLab-Overview_VbIPMdm.png", "date": "2024-09-26T11:05:00+02:00", "start": "11:05", "end": "2024-09-26T11:35:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-47030-datalab-bridging-scientific-and-industrial-worlds-for-advanced-signal-and-image-processing", "url": "https://pretalx.com/pydata-paris-2024/talk/WTDVCC/", "title": "DataLab: Bridging Scientific and Industrial Worlds for Advanced Signal and Image Processing", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "The video is available here: https://www.youtube.com/watch?v=yn1bR-BVfn8&list=PLGVZCDnMOq0pKya8gksd00ennKuyoH7v7&index=37\n\nThis talk introduces [DataLab](https://datalab-platform.com), a unique open-source platform for signal and image processing, seamlessly integrating scientific and industrial applications.\n\nThe main objective of this talk is to show how [DataLab](https://datalab-platform.com) may be used as a complementary tool alongside with Jupyter notebooks or an IDE (e.g., [Spyder](https://www.spyder-ide.org/)), and how it can be extended with custom Python scripts or applications.", "description": "[DataLab](https://datalab-platform.com) offers a practical approach to signal and image processing, tailored for the scientific community. This platform, grounded in Python and leveraging the rich ecosystem of scientific libraries (e.g. NumPy, SciPy, scikit-image, OpenCV, PlotPyStack), supports a range of research activities.\n\nThis talk will explore the main use cases of [DataLab](https://datalab-platform.com):\n- Processing and visualizing your signals and images (use DataLab as generic processing software)\n- Prototyping your processing pipeline (direct communication with your IDE or notebook)\n- Debugging your processing application (direct communication with DataLab from your application)\n- Enhancing your application with processing features (use DataLab as a library, or as a companion to your application)\n\n***Real-World Scientific Applications***\n\nThis segment will present how [DataLab](https://datalab-platform.com) is used in various scientific fields. We will explore examples like spectral data analysis in chemistry, measuring laser beam sizes in physics, or identifying biological structures in medical imaging. These instances will demonstrate [DataLab](https://datalab-platform.com)'s adaptability and utility in real research scenarios, as a rapid prototyping tool for signal and image processing as well as a customizable platform for data visualization.\n\n***Extensibility and Automation in Scientific Research***\n\n[DataLab](https://datalab-platform.com)'s extensible nature allows researchers to tailor it to their specific needs. This part of the talk will illustrate how custom plugins and macros can enhance research capabilities, while its automation features streamline repetitive tasks, improving efficiency in scientific workflows.\n\n***Bridging Scientific Research with Industrial Processes***\n\nA case study will be presented, illustrating [DataLab](https://datalab-platform.com)'s application in a scientific setting, where it also interfaces with industrial control systems. This example will showcase [DataLab](https://datalab-platform.com)'s versatility and its potential to facilitate data processing in cross-disciplinary projects.", "recording_license": "", "do_not_record": false, "persons": [{"code": "UPWDWG", "name": "Pierre Raybaut", "avatar": "https://pretalx.com/media/avatars/UPWDWG_330Mh0R.webp", "biography": "[Pierre Raybaut](https://pierreraybaut.github.io/) is a long-term advocate of Python in a scientific context, renowned as the creator of [Spyder](https://www.spyder-ide.org/), the Scientific Python IDE, and other pivotal projects like Python(x,y) and [WinPython](https://winpython.github.io/). These tools have been instrumental in making Python a leading language for scientific computing.\n\nPierre's academic journey began with an engineer's degree from the Institut d\u2019Optique Graduate School, specializing in laser physics. He further advanced his expertise by earning a PhD in optics and photonics from Universit\u00e9 Paris-Saclay, where he developed software for simulating regenerative amplification in ultra-short pulse lasers.\n\nProfessionally, Pierre has held diverse and impactful roles. He served as a research engineer at [THALES Avionics](https://www.thalesgroup.com/en/markets/aerospace/flight-deck-avionics-equipment-functions), a principal software developer at [CEA](https://www.cea.fr/english) (the French Alternative Energies and Atomic Energy Commission), and managed the [Laser M\u00e9gaJoule timing and fiducial system](https://accelconf.web.cern.ch/ICALEPCS2013/papers/thcoca05.pdf) project. Eventually, he became the Head of a Research Laboratory at CEA before transitioning to Codra, an industrial software company, where he currently holds the position of Chief Technology Officer (CTO).\n\nBeyond his work on [Spyder](https://www.spyder-ide.org/), Pierre is deeply involved in the open-source software community. He has created tools such as [guidata](https://guidata.readthedocs.io/en/latest/), [PlotPy](https://plotpy.readthedocs.io/en/latest/), [PlotPyStack](https://github.com/PlotPyStack) and [DataLab](https://datalab-platform.com), and has contributed to numerous other projects.", "public_name": "Pierre Raybaut", "guid": "2574c596-3855-5415-9ccf-be1a633214d8", "url": "https://pretalx.com/pydata-paris-2024/speaker/UPWDWG/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/WTDVCC/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/WTDVCC/", "attachments": []}, {"guid": "d5c7a7a7-e497-58a6-b0d4-664ffed5b1b4", "code": "Z989SU", "id": 48483, "logo": null, "date": "2024-09-26T11:40:00+02:00", "start": "11:40", "end": "2024-09-26T12:10:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-48483-onyxia-a-user-centric-interface-for-data-scientists-in-the-cloud-age", "url": "https://pretalx.com/pydata-paris-2024/talk/Z989SU/", "title": "Onyxia: A User-Centric Interface for Data Scientists in the Cloud Age", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "In this talk, we'll look into why Insee had to go beyond usual tools like JupyterHub. With data science growing, it has become important to have tools that are easy to use, can change as needed, and help people work together. The opensource software Onyxia brings a new answer by offering a user-friendly way to boost creativity in a data environment that uses massively containerization and object storage.", "description": "https://onyxia.sh is an opensource software developed by the French public service that aims at providing a state-of-the-art working environment (python, R) for data teams based on kubernetes and S3.\n\nIn this talk, we will present :\n- why we think that containerisation and object storage is mandatory to make a scalable data platform.\n- why we developped Onyxia and our principles to make it sustainable\n- how we design it and how we are trying to create a community\n\nLast but not least, you may learn that you are eligible to use our free, open-innovation platform named SSPCloud.", "recording_license": "", "do_not_record": false, "persons": [{"code": "CLLWYM", "name": "Comte Fr\u00e9d\u00e9ric", "avatar": null, "biography": "After 8 years of restricting datascientist because of my role in cybersecurity, I changed direction to empower them instead.", "public_name": "Comte Fr\u00e9d\u00e9ric", "guid": "d90a50f7-0933-57a6-b4ab-f6645f82802e", "url": "https://pretalx.com/pydata-paris-2024/speaker/CLLWYM/"}, {"code": "UNK9EL", "name": "Joseph Garrone", "avatar": "https://pretalx.com/media/avatars/UNK9EL_dtpWKii.webp", "biography": "I am the lead devlopper of Onyxia Datalab and the creator of a set of open source libraries like Keycloakify, tss-react or Denoify.", "public_name": "Joseph Garrone", "guid": "2ebc71a2-8a3b-5ba2-b66b-62bb1321c283", "url": "https://pretalx.com/pydata-paris-2024/speaker/UNK9EL/"}, {"code": "LSGCFY", "name": "Lino Galiana", "avatar": "https://pretalx.com/media/avatars/LSGCFY_7IKgE84.webp", "biography": "I'm a data scientist in the innovation lab at Insee, the French statistics institute. I'm also very involved in teaching Python, mainly in engineering courses: for beginners (https://pythonds.linogaliana.fr/) or for data scientists wishing to discover cloud technologies (https://ensae-reproductibilite.github.io/website/).", "public_name": "Lino Galiana", "guid": "f4615e73-be27-5591-8600-db1226157fce", "url": "https://pretalx.com/pydata-paris-2024/speaker/LSGCFY/"}, {"code": "WCMYTK", "name": "Quentin Gaudel", "avatar": null, "biography": "I am a cloud service architect at Mercator Ocean international, developing a public core infrastructure for the European Digital Twin Ocean.", "public_name": "Quentin Gaudel", "guid": "6a369765-6c59-5275-894f-01101624a57d", "url": "https://pretalx.com/pydata-paris-2024/speaker/WCMYTK/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/Z989SU/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/Z989SU/", "attachments": []}, {"guid": "8025d8ea-0e45-5544-b607-3bff68497128", "code": "WHXMTY", "id": 47956, "logo": null, "date": "2024-09-26T13:50:00+02:00", "start": "13:50", "end": "2024-09-26T14:20:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-47956-towards-a-deeper-understanding-of-retrieval-and-vector-databases", "url": "https://pretalx.com/pydata-paris-2024/talk/WHXMTY/", "title": "Towards a deeper understanding of retrieval and vector databases", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Retrieval is the process of searching for a given item (image, text, \u2026) in a large database that are similar to one or more query items. A classical approach is to transform the database items and the query item into vectors (also called embeddings) with a trained model so that they can be compared via a distance metric. It has many applications in various fields, e.g. to build a visual recommendation system like Google Lens or a RAG (Retrieval Augmented Generation), a technique used to inject specific knowledge into LLMs depending on the query. \nVector databases ease the management, serving and retrieval of the vectors in production and implement efficient indexes, to rapidly search through millions of vectors. They gained a lot of attention over the past year, due to the rise of LLMs and RAGs.\n\nAlthough people working with LLMs are increasingly familiar with the basic principles of vector databases, the finer details and nuances often remain obscure. This lack of clarity hinders the ability to make optimal use of these systems.\n\nIn this talk, we will detail two examples of real-life projects (Deduplication of real estate adverts using the image embedding model DinoV2 and RAG for a medical company using the text embedding model Ada-2) and deep dive into retrieval and vector databases to demystify the key aspects and highlight the limitations: HSNW index, comparison of the providers, metadata filtering (the related plunge of performance when filtering too many nodes and how indexing partially helps it), partitioning, reciprocal rank fusion, the performance and limitations of the representations created by SOTA image and text embedding models, \u2026", "description": "Retrieval is the process of searching for a given item (image, text, \u2026) in a large database that are similar to one or more query items. A classical approach is to transform the database items and the query item into vectors (also called embeddings) with a trained model so that they can be compared via a distance metric. It has many applications in various fields, e.g. to build a visual recommendation system like Google Lens or a RAG (Retrieval Augmented Generation), a technique consisting of retrieving the documents the most similar to a given question, to then answer the question with an LLM given the retrieved documents. \n\nVector databases ease the management, serving and retrieval of the vectors in production and implement efficient indexes, to rapidly search through millions of vectors. They gained a lot of attention over the past year, due to the rise of LLMs and RAGs.\n\nAlthough people working with LLMs are increasingly familiar with the basic principles of vector databases, the finer details and nuances often remain obscure. This lack of clarity hinders the ability to make optimal use of these systems. For instance which differences matter between the different providers? How mature metadata filtering is, when to use it and what are the limitations? What is partitioning and what is the best way to do it? Which functionalities beyond simple vector search can be helpful? \u2026\n\nIn this talk, we will clarify these grey areas to help better leverage these systems. More specifically, we will:\n\n- 1/ Introduce the main concepts:\n    - How retrieval works\n    - What are vector databases\n    - What are the use cases. We will introduce two real-life projects we did using vector databases, which will allow us to provide practical examples through the talk\n        - Deduplication of real estate adverts using the image embedding model DinoV2\n        - RAG for a medical company using the text embedding model Ada-2\n- 2/ Highlight the pros and cons of the different providers, with a highlight on the differences between dedicated vector databases and more general-purpose databases.\n- 3/ Deep dive into the technicality of vector search:\n    - How does the HSNW index work and how to configure it\n    - The necessity of metadata filtering and the related limitations: mostly when filtering out more than 80-90% of the nodes the precision plunges. Hence indexing becomes necessary but it is still imperfect in complex cases.\n    - The performance and limitations of the representations created by state-of-the-art image and text embedding models", "recording_license": "", "do_not_record": false, "persons": [{"code": "B893GM", "name": "No\u00e9 Achache", "avatar": "https://pretalx.com/media/avatars/B893GM_9I9v5xU.webp", "biography": "I am an Engineering Manager (for Data Science projects) at Sicara, where I worked on a wide range of projects mostly related to vector databases, computer vision, prediction with structured data and more recently LLMs.\nI am currently leading the GenAI development in the company.\nYou can find all my talks and articles here: https://www.sicara.fr/en/noe-achache, e.g.\n- https://www.sicara.fr/blog-technique/how-to-choose-your-vector-database-in-2023\n- https://www.youtube.com/watch?v=aX_hdQEintc", "public_name": "No\u00e9 Achache", "guid": "59d86e59-29a5-5cdc-a122-a110fd5d804d", "url": "https://pretalx.com/pydata-paris-2024/speaker/B893GM/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/WHXMTY/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/WHXMTY/", "attachments": []}, {"guid": "0d13cb07-e9df-5bb1-9070-cd46e2fe43cf", "code": "QWXJTN", "id": 48149, "logo": null, "date": "2024-09-26T14:25:00+02:00", "start": "14:25", "end": "2024-09-26T14:55:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-48149-processing-medical-images-at-scale-on-the-cloud", "url": "https://pretalx.com/pydata-paris-2024/talk/QWXJTN/", "title": "Processing medical images at scale on the cloud", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "The MedTech industry is undergoing a revolutionary transformation with continuous innovations promising greater precision, efficiency, and accessibility. In particular oncology, a branch of medicine that focuses on cancer, will benefit immensely from these new technologies, which may enable clinicians to detect cancer earlier and increase chances of survival. Detecting cancerous cells in microscopic photography of cells (Whole Slide Images, aka WSIs) is usually done with segmentation algorithms, which neural networks (NNs) are very good at. While using ML and NNs for image segmentation is a fairly standard task with established solutions, doing it on WSIs is a different kettle of fish. Most training pipelines and systems have been designed for analytics, meaning huge columns of small individual datums. In the case of WSIs, a single image is so huge that its file can be up to dozens of gigabytes. To allow innovation in medical imaging with AI, we need efficient and affordable ways to store and process these WSIs at scale.", "description": "In this presentation, we look at the challenges encountered in processing large-scale medical images, particularly in oncology, where images can be as large as several gigabytes. We discuss the limitations faced by most companies in accessing supercomputing resources and the necessity of cloud-based solutions. The separation of storage and compute, coupled with the complexities of distributed computing, presents significant hurdles. We explore methods and tools to address these challenges, enabling data scientists to iterate and experiment efficiently. Drawing from our experience at Modus Create (formerly Tweag) working with Kaiko.ai, we will share insights into building an AI research platform tailored to the fight against cancer.\n\n* Introduction, Whole Slide Images and detecting cancer (5 min)\n* The challenge of training Neural Networks on Whole Slide Images on the cloud (5 min)\n* Building blocks for an AI Research Platform for oncology (10 min)\n* Future developments (5 min)\n* Q&A (5 min)\n\nThis talk assumes minimal Python and Cloud knowledge. No specific medical knowledge is required.", "recording_license": "", "do_not_record": false, "persons": [{"code": "E3RVJT", "name": "Guillaume Desforges", "avatar": "https://pretalx.com/media/avatars/E3RVJT_o4i5fJE.webp", "biography": "Senior Software Engineering at Modus Create with an academic background in mathematics, statistics and AI and professional experience in data engineering at scale. Leads the Data Engineering Technical Group of Modus Create's OSPO.", "public_name": "Guillaume Desforges", "guid": "d9549744-ffda-5f69-9cab-f528bbb221dd", "url": "https://pretalx.com/pydata-paris-2024/speaker/E3RVJT/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/QWXJTN/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/QWXJTN/", "attachments": []}, {"guid": "5a55b9ab-2841-58dc-be5b-c4c33381755e", "code": "ZREEJS", "id": 48586, "logo": null, "date": "2024-09-26T15:00:00+02:00", "start": "15:00", "end": "2024-09-26T15:30:00+02:00", "duration": "00:30", "room": "Louis Armand 2 - Ouest", "slug": "pydata-paris-2024-48586-mlops-at-renault-group-a-generic-pipeline-for-scalable-deployment", "url": "https://pretalx.com/pydata-paris-2024/talk/ZREEJS/", "title": "MLOps at Renault Group: A Generic Pipeline for Scalable Deployment", "subtitle": "", "track": null, "type": "Talk", "language": "en", "abstract": "Scaling machine learning at large organizations like Renault Group presents unique challenges, in terms of scales, legal requirements, and diversity of use cases. Data scientists require streamlined workflows and automated processes to efficiently deploy models into production. We present an MLOps pipeline based on python Kubeflow and GCP Vertex AI API designed specifically for this purpose. It enables data scientists to focus on code development for pre-processing, training, evaluation, and prediction. This MLOPS pipeline is a cornerstone of the AI@Scale program, which aims to roll out AI across the Group.\n\nWe choose a Python-first approach, allowing Data scientists to focus purely on writing preprocessing or ML oriented Python code, also allowing data retrieval through SQL queries. The pipeline addresses key questions such as prediction type (batch or API), model versioning, resource allocation, drift monitoring, and alert generation. It favors faster time to market with automated deployment and infrastructure management. Although we encountered pitfalls and design difficulties, that we will discuss during the presentation, this pipeline integrates with a CI/CD process, ensuring efficient and automated model deployment and serving.\n\nFinally, this MLOps solution empowers Renault data scientists to seamlessly translate innovative models into production, and smoothen the development of scalable, and impactful AI-driven solutions.", "description": "Topic: This presentation delves into the development and implementation of a robust MLOps pipeline at Renault, designed to address the unique challenges of scaling machine learning deployments in large, complex organizations.\nAudience: This talk is primarily aimed at data scientists, ML engineers, and anyone interested in:\n\u2022\tLearning about efficient MLOps implementation for large-scale deployments.\n\u2022\tUtilizing Python for comprehensive ML workflows, including data retrieval with SQL.\n\u2022\tAddressing key considerations for production-ready model deployment and management.\n\nAttendees will gain insights into:\n\u2022\tImplementing an MLOps pipeline tailored for real-world use cases.\n\u2022\tEfficiently automating machine learning workflows for production environments.\n\u2022\tUtilizing Python code for all stages of model development and deployment.\n\u2022\tAddressing key considerations for scalable ML deployments at large organizations.\n\u2022\tPotential pitfalls in creating a custom MLOPS solutions.\n\nBackground Knowledge:\n\u2022\tBasic understanding of Machine Learning and Python programming is recommended.\n\u2022\tFamiliarity with MLOps concepts and CI/CD pipelines would be beneficial.", "recording_license": "", "do_not_record": false, "persons": [{"code": "JKYXNL", "name": "Alix Tiran-Cappello", "avatar": "https://pretalx.com/media/avatars/JKYXNL_QKeSp69.webp", "biography": "I have been working as a Data Scientist at Renault Digital for 3 years, focusing on applications for the industry and manufacturing of electric cars. More recently, I became involved in the development of a MLOPS pipeline for Renault, and started to act as a relay with the DevOps team. Finally, I also contribute to the development Data Science best-practices with dedicated mentoring sessions.", "public_name": "Alix Tiran-Cappello", "guid": "2461b97b-fb04-5e12-842a-903971ac4e6b", "url": "https://pretalx.com/pydata-paris-2024/speaker/JKYXNL/"}, {"code": "7ZAKLJ", "name": "Alexandre Carton", "avatar": "https://pretalx.com/media/avatars/7ZAKLJ_ijdKmkh.webp", "biography": "I'm a data scientist and ML engineer at Renault, working on putting our ML models into production.", "public_name": "Alexandre Carton", "guid": "7a6827c7-af70-5e9a-94c7-afd95cf9876e", "url": "https://pretalx.com/pydata-paris-2024/speaker/7ZAKLJ/"}], "links": [], "feedback_url": "https://pretalx.com/pydata-paris-2024/talk/ZREEJS/feedback/", "origin_url": "https://pretalx.com/pydata-paris-2024/talk/ZREEJS/", "attachments": []}]}}, {"index": 3, "date": "2024-09-27", "day_start": "2024-09-27T04:00:00+02:00", "day_end": "2024-09-28T03:59:00+02:00", "rooms": {}}]}}}