Track your Data Science
with skore

Evaluate, compare, and track your ML experiments. Built by the team that created and maintains scikit-learn. One line of code, comprehensive model evaluation, smart methodological guidance.

Try skore for free

Who we are

Probabl maintains scikit-learn

Our team builds and maintains robust machine learning algorithms while keeping them simple and accessible, staying true to scikit-learn's founding philosophy of making predictive data analysis tools efficient and reusable in any context.

Beyond scikit-learn, we're expanding our impact across the entire data science pipeline: from where your data lives, with skrub handling the messy reality of heterogeneous tables and dataframes, to guiding you through the maze of experimentation with skore, helping data scientists move faster from raw data to validated, production-ready models.

scikit-learn core team employed by Probabl

GV

Gaël Varoquaux

Co-founder, CSO

OG

Olivier Grisel

Co-founder, Core maintainer

GL

Guillaume Lemaitre

Co-founder, Core maintainer

JdB

Jérémie du Boisberranger

Co-founder, Core maintainer

AJ

Adrin Jalali

Co-founder, Core maintainer

LE

Loïc Estève

Co-founder, Core maintainer

FG

François Goupil

Co-founder, Core contributor

AA

Arturo Amor

Co-founder, Core contributor

SS

Stefanie Senger

Core maintainer

See the full team on scikit-learn.org

The story

From an open-source initiative to the world's most used ML library

2007

scikit-learn is created

David Cournapeau publishes scikits.learn as a Google Summer of Code project.

2010

First public release

Gaël Varoquaux, Fabian Pedregosa, Alexandre Gramfort and Vincent Michel at Inria take leadership. Version 0.1 beta ships on February 1st. The project gets renamed to scikit-learn.

2011

JMLR paper published

The foundational paper is published in the Journal of Machine Learning Research. The community grows to hundreds of contributors. First coding sprint in Spain.

2018

Consortium at the Inria Foundation

A consortium of corporate sponsors is created to fund scikit-learn's long-term maintenance: Microsoft, BCG, AXA, BNP Paribas Cardif, Intel, NVIDIA, Dataiku. Later joined by Chanel, Michelin, and Hugging Face among others. 42 million documentation visits that year alone.

2021

Version 1.0

scikit-learn 1.0.0 ships after 2,100+ merged pull requests. A landmark release after 11 years of continuous development.

2023

Probabl is founded

The scikit-learn core team at Inria spins off to create Probabl, with support from the French government's France 2030 program and Inria Participations.

2024

Practitioner certifications and skolar

Launch of the scikit-learn practitioner certifications and skolar, the reference learning platform for open-source machine learning.

2025

€13M seed, a European record for open source

Probabl raises €13M, the largest seed round for an open-source company in Europe. scikit-learn 1.8 ships with native GPU support via the Array API.

2026

skore launches, scikit-learn Central goes live

Probabl officially ships skore, the Data Science platform by the scikit-learn founders. scikit-learn Central launches as the ecosystem catalog and use case explorer. 4.1 billion cumulative downloads reached.

What you get

Tools and expertise from the source

We build products that encode the methodology and best practices our maintainers have developed over 15 years. Every data scientist can benefit from that depth, from day one.

skore

The Data Science platform by the scikit-learn founders

Example

import skore, skrub
from sklearn.linear_model import Ridge
model = skrub.tabular_pipeline(Ridge())
report = skore.evaluate(model, df, y, splitter=0.2)
report

import skore, skrub
from sklearn.linear_model import Ridge
model = skrub.tabular_pipeline(Ridge())
report = skore.evaluate(model, df, y, splitter=5)
report

import skore, skrub
from sklearn.linear_model import Ridge
from sklearn.ensemble import RandomForestRegressor
models = [
    skrub.tabular_pipeline(Ridge()),
    skrub.tabular_pipeline(RandomForestRegressor()),
]
comparison_report = skore.evaluate(models, df, y, splitter=5)
comparison_report

Evaluate any scikit-learn compatible model in one line of code. skore automatically generates the right metrics, feature importance plots, and diagnostics for your use case, with smart methodological warnings that catch common pitfalls before they reach production.

Automated evaluation reports

Cross-validation insights

Model comparison

Methodological guidance

Try skore, free and open source

skolar

The official scikit-learn training and certification

Hands-on courses built by scikit-learn core developers, the same people who created the MOOC that reached 40,000+ learners worldwide. Validate your ML expertise with the only official scikit-learn certification.

Start learning on skolar