scikit-learn
Provide simple and efficient tools for predictive data analysis, accessible to everybody and reusable in various contexts.
At a Glance
- Enterprise data science teams
- Academic researchers
- Software developers
- AI startups
AI Tools by scikit-learn
(1)scikit-learn
Open Source Python ML Library
Discussions
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Latest News
Probabl announces scikit-learn Roadmap for 2026, focusing on GPU support and tree-based model improvements.
scikit-learn 1.8.0 released with expanded metadata routing and improved estimator validation.
Probabl raises additional €5.5M in seed funding to expand Europe's open-source AI ecosystem.
scikit-learn 1.7.0 brings native pandas support and better integration with scientific Python tools.
Products & Services
Comprehensive open-source machine learning library for Python featuring classification, regression, clustering, and more.
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
David Cournapeau
Started scikit-learn in 2007 as a Google Summer of Code project. Software engineer with a background in neuroimaging and data science.
Matthieu Brucher
Early contributor who started working on the project in 2007.
Gaël Varoquaux
Research Director at Inria. Co-founded the project's revival in 2010. Expert in neuroimaging and machine learning.
Alexandre Gramfort
Researcher at Inria. Key early contributor and leader since 2010. Specialist in brain imaging and signal processing.
Fabian Pedregosa
Lead maintainer during the project's early years (2010-2012). Researcher at Google DeepMind (previously).
Vincent Michel
Early maintainer at Inria who helped make the first public release in 2010.
Executive Team
Gaël Varoquaux
Technical Committee Member & Co-founder
Research Director at Inria, leader in the scientific Python ecosystem.
Olivier Grisel
Technical Committee Member
Software Engineer at Inria, core developer of scikit-learn since the early days.
Board of Directors
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 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
BSD-3 Clause License, free for all uses.
Target Markets
- Enterprise data science teams
- Academic researchers
- Software developers
- AI startups
- Predictive data analysis
- Scientific research and experimentation
- Industrial machine learning pipelines
- Educational tool for machine learning
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