The next Python Exchange is coming up!
Our Guest Panelist will be:
Guillaume Lemaitre
“Developments in the scikit-learn Ecosystem: Going Beyond model.fit(X, y).predict(X)”
Scikit-learn is one of the de facto libraries when it comes to predictive modeling with tabular data. For over a decade, it has provided traditional and reliable algorithms to address data science problems. While it excels at model fitting and prediction, these stages represent only a small portion of a data science project and are relatively well-defined. Many data scientists are familiar with the notion that 90% of their time is spent on preprocessing, while the modeling stage takes up only 10% of their efforts. Additionally, tracking and organizing experiments, as well as transitioning from experimentation to production, can be challenging. This talk aims to shed light on recent developments and efforts within the scikit-learn ecosystem. We will provide an overview of the following tools through a series of short notebook demos.
- scikit-learn to provide some context.
- skrub, which offers tools to prepare your tabular data and aims to bridge the gap between the database world and the scikit-learn modeling environment.
- skops, which provides tools for transitioning from experimentation to production settings.
- skore, which aims to provide guidance throughout the lifecycle of a data science project by abstracting the above tools, making them more general and less opinionated.
About Our Guest
Guillaume Lemaitre is Chief Machine Learning Officer and an open-source software engineer at Probabl. He is a core maintainer of several packages from the scikit-learn ecosystem such as scikit-learn, skrub, skore, and imbalanced-learn. He holds a PhD in computer science / medical imaging.