📢 PyData Milano is back with another exciting event, hosted at Amazon Italia! This time, we’re diving into two major topics shaping the future of data science and software development. 📍 Location: Amazon Italia, Viale Monte Grappa 3/5, 20124 Milano 🕕 Time: 18:30

Agenda

18:30 – Doors open 19:00 – Welcome from PyData Milano 19:10 – Talk 1: Developments in the Scikit-Learn Ecosystem – Marie Sacksick & Guillaume Lemaître (Probabl.) 19:50 – Talk 2: AI Coding Agents and How to Code Them – Alex Shershebnev (Zencoder) 20:30 – Networking & Aperitivo 🍹

Talk Details

Developments in the Scikit-Learn Ecosystem: Going Beyond .fit(X, y).predict(X)

Scikit-learn has long been the go-to library for predictive modeling, but data science is much more than just training models. In this talk, we’ll explore new tools that enhance the entire machine learning workflow—from data preparation with skrub, to experiment tracking with skore, to production-ready model deployment with skops. Expect demos, insights, and a fresh perspective on how the scikit-learn ecosystem is evolving. 👩‍💻 Speakers: Marie Sacksick (Product Engineer @ Probabl., WiMLDS Paris) Guillaume Lemaître (Open-Source Engineer @ Probabl.)

AI Coding Agents and How to Code Them

AI coding agents promise a new paradigm in software development, going beyond simple autocomplete to autonomous task execution. In this talk, we’ll break down what makes them different from traditional coding assistants, showcase live coding examples, and explore how they might shape the future of programming. By the end, you’ll be ready to integrate AI agents into your workflow—starting now. 🧑‍💻 Speaker: Alex Shershebnev (Head of ML/DevOps @ Zencoder) Join us for a deep dive into the future of machine learning and AI-powered coding, and don’t miss the chance to network over an aperitivo after the talks! 🔗 Register now and spread the word! 🚀

PyDataMilano #MachineLearning #AI #ScikitLearn #DataScience #AIAgents

In this podcast episode, we talked with Guillaume Lemaître about navigating scikit-learn and imbalanced-learn.

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Navigating scikit-learn and imbalanced-learn - Guillaume Lemaître

About the event

Outline:

• Working as a core developer on scikit-learn
• Working as a core developer on imbalance-learn

About the speaker

Guillaume is an open-source software engineer working at :probably. He is a core maintainer of the scikit-learn and imbalanced-learn libraries. He holds a PhD in medical imaging.

DataTalks.Club is the place to talk about data. Join our slack community!

Mark your calendar for the next session of the PyData Paris Meetup, on March 21st 2024. This Meetup will be hosted by Scaleway, Europe's empowering cloud provider at the Iliad group office, 16 rue de la ville l'evêque 75008 Paris. The speakers for this session, that will be dedicated to Taipy are Alexandre Sajus and Florian Jacta.

Schedule 7:00pm - 7:15pm: Community announcements & short address by Fred Bardolle, Lead Product Manager AI at Scaleway. 7:15pm - 7:45pm: Get the best from your scikit-learn classifier: trusted probabilities and optimal binary decision, Guillaume Lemaître 7:45pm - 8:30pm: Deploy your Data Project on the Web using only Python, Alexandre Sajus & Florian Jacta 8:30pm - 9:30pm: Buffet

Speakers

Alexandre Sajus is a customer success engineer at Taipy. He graduated with a Master's of Engineering from Centrale Paris. Florian Jacta is a data scientist and community manager at Taipy.

Guillaume Lemaitre is an open-source scientific software developer at :probabl. and a core developer of the scikit-learn project.

Abstracts

Deploy your Data Project on the Web using only Python, Alexandre Sajus & Florian Jacta In the Python ecosystem, many packages are available for running algorithms, training models, and visualizing data. Despite this, over 85% of data science projects stay at the proof-of-concept stage and never reach the production stage. With Taipy, Python developers can build great pilots as well as stunning production-ready web applications designed for end-users.

Get the best from your scikit-learn classifier: trusted probabilties and optimal binary decision, Guillaume Lemaitre When operating a classifier in a production setting (i.e. predictive phase), practitioners are interested in potentially two different outputs: a "hard" decision used to leverage a business decision or/and a "soft" decision to get a confidence score linked to each potential decision (e.g. usually related to class probabilities). Scikit-learn does not provide any flexibility to go from "soft" to "hard" predictions: it uses a cut-off point at a confidence score of 0.5 (or 0 when using decision_function) to get class labels. However, optimizing a classifier to get a confidence score close to the true probabilities (i.e. a calibrated classifier) does not guarantee to obtain accurate "hard" predictions using this heuristic. Reversely, training a classifier for an optimum "hard" prediction accuracy (with the cut-off constraint at 0.5) does not guarantee obtaining a calibrated classifier. In this talk, we will present a new scikit-learn meta-estimator allowing us to get the best of the two worlds: a calibrated classifier providing optimum "hard" predictions. This meta-estimator will land in a future version of scikit-learn: https://github.com/scikit-learn/scikit-learn/pull/26120. We will provide some insights regarding the way to obtain accurate probabilities and predictions and also illustrate how to use in practice this model on different use cases: cost-sensitive problems and imbalanced classification problems.