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    1. Home
    2. Developers
    3. scikit-learn

    scikit-learn

    Provide simple and efficient tools for predictive data analysis, accessible to everybody and reusable in various contexts.

    Visit Website

    At a Glance

    1Tool Listed
    2Products
    6Capabilities
    Discussions
    Inria, PalaiseauHeadquarters
    2007Est.
    25Employees
    Focus Areas
    AI Development Libraries
    Data Processing
    Predictive Analytics
    Connect
    Latest News
    Probabl announces scikit-learn Roadmap for 2026, focusing on GPU support and tree-based model improvements.Mar 11, 2026
    scikit-learn 1.8.0 released with expanded metadata routing and improved estimator validation.Dec 15, 2025
    Markets
    • Enterprise data science teams
    • Academic researchers
    • Software developers
    • AI startups

    AI Tools by scikit-learn

    (1)
    View scikit-learn
    scikit-learn tool icon

    scikit-learn

    Open Source Python ML Library

    AI Dev LibrariesData ProcessingPredictive Analyt.

    Discussions

    No discussions yet

    Be the first to start a discussion about scikit-learn

    Latest News

    03/11/2026

    Probabl announces scikit-learn Roadmap for 2026, focusing on GPU support and tree-based model improvements.

    blog.probabl.ai
    12/15/2025

    scikit-learn 1.8.0 released with expanded metadata routing and improved estimator validation.

    scikit-learn.org
    10/14/2025

    Probabl raises additional €5.5M in seed funding to expand Europe's open-source AI ecosystem.

    inria.fr
    09/01/2025

    scikit-learn 1.7.0 brings native pandas support and better integration with scientific Python tools.

    scikit-learn.org

    Products & Services

    2
    scikit-learn Library
    2010-02-01

    Comprehensive open-source machine learning library for Python featuring classification, regression, clustering, and more.

    Skore
    2024

    A methodology and tooling layer built by Probabl to manage AI experiments and bridge coding with production.

    Market Position

    The industry-standard machine learning library for Python, known for its stability, documentation, and focus on classical algorithms vs deep learning.

    Leadership

    Founders

    DC

    David Cournapeau

    Started scikit-learn in 2007 as a Google Summer of Code project. Software engineer with a background in neuroimaging and data science.

    MB

    Matthieu Brucher

    Early contributor who started working on the project in 2007.

    GV

    Gaël Varoquaux

    Research Director at Inria. Co-founded the project's revival in 2010. Expert in neuroimaging and machine learning.

    AG

    Alexandre Gramfort

    Researcher at Inria. Key early contributor and leader since 2010. Specialist in brain imaging and signal processing.

    FP

    Fabian Pedregosa

    Lead maintainer during the project's early years (2010-2012). Researcher at Google DeepMind (previously).

    VM

    Vincent Michel

    Early maintainer at Inria who helped make the first public release in 2010.

    Executive Team

    GV

    Gaël Varoquaux

    Technical Committee Member & Co-founder

    Research Director at Inria, leader in the scientific Python ecosystem.

    OG

    Olivier Grisel

    Technical Committee Member

    Software Engineer at Inria, core developer of scikit-learn since the early days.

    Board of Directors

    TD
    Terry Deshler
    NumFOCUS Board of Directors (Fiscal Sponsor)
    IF
    Inria Foundation
    Hosts the scikit-learn Consortium
    SA
    Sponsor Advisory Board
    Representatives from Microsoft, NVIDIA, Hugging Face, etc.

    Founding Story

    Started as a Google Summer of Code project in 2007. It was later adopted and professionalized by researchers at Inria in 2010 to create a unified machine learning library for the Python ecosystem.

    Business Model

    Revenue
    Supported by a Consortium with over 10 major corporate sponsors. Probabl.ai raised €18.5M in seed funding by late 2025.

    Revenue Model

    Open source project supported by sponsorships, grants (NumFOCUS, CZI), and the scikit-learn Consortium. Commercial support and services are provided by Probabl.ai.

    Pricing Tiers

    Open Source
    $0

    BSD-3 Clause License, free for all uses.

    Private (Probabl); Project is Open Source

    Target Markets

    Industries & Segments
    • Enterprise data science teams
    • Academic researchers
    • Software developers
    • AI startups
    Use Cases
    • Predictive data analysis
    • Scientific research and experimentation
    • Industrial machine learning pipelines
    • Educational tool for machine learning
    Notable Customers
    • Spotify
    • Evernote
    • Booking.com
    • J.P. Morgan

    Quick Facts

    Headquarters
    Inria, Palaiseau, France
    Founded
    2007
    Entity Type
    Open Source Project
    Employees
    25
    Total Funding
    €18.5M (Probabl Seed) + Ongoing Consortium Sponsorships
    Investors
    Bpifrance, Inria
    Office Locations
    Paris
    Palaiseau

    Funding History

    Seed (Probabl)€13M
    2024-02
    Bpifrance (French Tech Souveraineté)
    Capgemini
    CMA CGM
    Seed Extension (Probabl)€5.5M
    2025-10
    Bpifrance

    History & Milestones

    2025

    Release of version 1.8.0, introducing enhanced GPU support via Array API and metadata routing.

    2024

    Launch of Probabl.ai, an Inria spin-off company dedicated to industrializing scikit-learn.

    2021

    Release of scikit-learn 1.0.0, a major versioning milestone.

    2018

    Creation of the scikit-learn Consortium at the Inria Foundation to provide financial support.

    2011

    Publication of the seminal JMLR paper 'Scikit-learn: Machine Learning in Python'.

    Key Capabilities

    6
    Supervised learning (Classification, Regression)
    Unsupervised learning (Clustering, PCA)
    Consistent and simple API (fit/predict)
    Integration with NumPy, SciPy, and Matplotlib
    Metadata routing and estimator validation
    GPU support via Array API

    Integrations & Partnerships

    Platform Integrations

    • Python
    • Jupyter
    • Pandas
    • PySpark
    • Dask
    • ONNX

    Key Partnerships

    NumFOCUS
    Scientific Python
    Hugging Face

    Connect

    Website
    scikit-learn.org
    GitHub
    scikit-learn
    LinkedIn
    scikit-learn
    YouTube
    UCJosFjYm0ZYVUARxuOZqnnw
    Discord
    h9qyrK8Jc8
    Facebook
    scikitlearnofficial
    Instagram
    @scikitlearnofficial
    TikTok
    @/scikit.learn
    Bluesky
    @scikit-learn.org
    Mastodon
    fosstodon.org/@sklearn

    AI Topics

    3

    scikit-learn focuses on these topics:

    AI Development Libraries(1)
    Data Processing(1)
    Predictive Analytics(1)
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