.. _funding:

Institutional support

Scikit-learn is a community driven project. However, a number of public institutions and private entities have contributed and keep on contributing to its success and sustainability.

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.. image:: images/inria-logo.jpg
  :target: https://www.inria.fr

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Since the inception of scikit-learn and for a good decade, `Inria
<https://www.inria.fr>`_ has been its main supporting pillar, as the stable
employer of many core-maintainers and as the host for the scikit-learn
consortium.

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.. image:: images/probabl.png
  :target: https://probabl.ai

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In 2023, Inria spun off `Probabl <https://probabl.ai>`_ as a mission driven
company to take scikit-learn beyond a research lab. All of the
scikit-learn core-maintainers employed by Inria have joined the spinoff as
co-founders, most as full-time employees.

Today, Probabl employs the following core and non-core contributors: Adrin
Jalali, Antoine Baker, Arturo Amor, François Goupil, Guillaume Lemaitre,
Jérémie du Boisberranger, Loïc Estève, Olivier Grisel, Shruti Nath and
Stefanie Senger, as well as Gaël Varoquaux.

The above financial commitments mean that Inria initially and now Probabl have been and are the main source of financial support for scikit-learn, completed by additional commitments detailed below. Cumulatively over a decade, these represent several millions of euros or dollars worth of financial participation.

Active financial participation (2026)

In addition to the above financial commitments, the following organizations financially support scikit-learn as follows:

.. |probabl| image:: images/probabl.png :target: https://probabl.ai

.. |wellcome| image:: images/wellcome-trust-small.png :target: https://wellcome.org

.. |czi| image:: images/czi-small.png :target: https://chanzuckerberg.com

.. |nvidia| image:: images/nvidia-small.png :target: https://www.nvidia.com

.. |nasa| image:: images/nasa-small.png :target: https://www.nasa.gov

.. |chanel| image:: images/chanel-small.png :target: https://www.chanel.com

.. |bnpparibasgroup| image:: images/bnp-paribas-small.png :target: https://group.bnpparibas/

.. |quansightlabs| image:: images/quansight-labs-small.png :target: https://labs.quansight.org

.. |michelin| image:: images/michelin-small.png :target: https://www.michelin.com

.. list-table:: :widths: 33 33 34 :header-rows: 1 :class: sk-funding-participation-table

• • 5 FTE or more
  • 0.5 FTE or more
  • less than 0.5 FTE
• • |probabl|
  • • |czi| |wellcome|
    • |nvidia|
    • |nasa| |quansightlabs|
    • |chanel|
  • • |bnpparibasgroup|
    • |michelin|

FTE stands for Full-Time Equivalent.

• The Chan-Zuckerberg Initiative <https://chanzuckerberg.com/>_ and Wellcome Trust <https://wellcome.org/>_ support the work of Lucy Liu, Dea Maria Leon, Anne Beyer and Francois Paugam through the Essential Open Source Software for Science (EOSS) <https://chanzuckerberg.com/eoss/>_ cycle 6.

• NVIDIA <https://nvidia.com>_ supports scikit-learn through their sponsorship and employs full-time core maintainer Tim Head.

• NASA <https://www.nasa.gov>_ supports work done by Quansight Labs and Probabl team members via the NASA ROSES grant 80NSSC25K7215: "Ensuring a fast and secure core for scientific Python".

• Quansight Labs <https://labs.quansight.org>_ funds Lucy Liu since 2022.

• Chanel <https://www.chanel.com>_ supports scikit-learn through a multi-year sponsorship, initially through the Inria foundation (2023-2024), now as a Gold Sponsor via Probabl (2024-2026).

• BNP Paribas Group <https://group.bnpparibas/>_ supports scikit-learn as a Silver Sponsor via Probabl (2025-2026).

• Michelin <https://www.michelin.com>_ supports scikit-learn as a Bronze Sponsor via Probabl (2025-2026).

Past Sponsors

Microsoft <https://microsoft.com/>_ funded Andreas Müller from 2020 to 2026.

APHP <https://aphp.fr/>, AXA <https://www.axa.fr/>, BCG <https://www.bcg.com/>, BNP-Paribas-Cardiff <https://www.bnpparibascardif.com/>, Dataiku <https://www.dataiku.com/>, Fujitsu <https://www.fujitsu.com/>, Hugging Face <https://huggingface.co/>, Intel <https://www.intel.com/>, Microsoft <https://microsoft.com/>, NVIDIA <https://nvidia.com> supported the project through their sponsorship via the Inria foundation between 2020 and 2024.

Tidelift <https://tidelift.com/>_ financially supported the project via their service agreement from 2023 to 2025.

Quansight Labs <https://labs.quansight.org>_ and NASA <https://www.nasa.gov>_ funded Meekail Zain in 2022 and 2023, and funded Thomas J. Fan from 2021 to 2023 via the NASA ROSES grant 80NSSC22K0405: "Reinforcing the Foundations of Scientific Python".

Columbia University <https://columbia.edu/>_ funded Andreas Müller (2016-2020).

The University of Sydney <https://sydney.edu.au/>_ funded Joel Nothman (2017-2021).

Andreas Müller received a grant to improve scikit-learn from the Alfred P. Sloan Foundation <https://sloan.org>_ . This grant supported the position of Nicolas Hug and Thomas J. Fan.

INRIA <https://www.inria.fr>_ has provided funding for Fabian Pedregosa (2010-2012), Jaques Grobler (2012-2013) and Olivier Grisel (2013-2017) to work on this project full-time. It also hosts coding sprints and other events.

Paris-Saclay Center for Data Science <http://www.datascience-paris-saclay.fr/>_ funded one year for a developer to work on the project full-time (2014-2015), 50% of the time of Guillaume Lemaitre (2016-2017) and 50% of the time of Joris van den Bossche (2017-2018).

NYU Moore-Sloan Data Science Environment <https://cds.nyu.edu/mooresloan/>_ funded Andreas Mueller (2014-2016) to work on this project. The Moore-Sloan Data Science Environment also funds several students to work on the project part-time.

Télécom Paristech <https://www.telecom-paristech.fr/>_ funded Manoj Kumar (2014), Tom Dupré la Tour (2015), Raghav RV (2015-2017), Thierry Guillemot (2016-2017) and Albert Thomas (2017) to work on scikit-learn.

The Labex DigiCosme <https://digicosme.lri.fr>_ funded Nicolas Goix (2015-2016), Tom Dupré la Tour (2015-2016 and 2017-2018), Mathurin Massias (2018-2019) to work part time on scikit-learn during their PhDs. It also funded a scikit-learn coding sprint in 2015.

The Chan-Zuckerberg Initiative <https://chanzuckerberg.com/>_ funded Nicolas Hug to work full-time on scikit-learn in 2020.

The following students were sponsored by Google <https://opensource.google/>_ to work on scikit-learn through the Google Summer of Code <https://en.wikipedia.org/wiki/Google_Summer_of_Code>_ program.

• 2007 - David Cournapeau
• 2011 - Vlad Niculae_
• 2012 - Vlad Niculae_, Immanuel Bayer
• 2013 - Kemal Eren, Nicolas Trésegnie
• 2014 - Hamzeh Alsalhi, Issam Laradji, Maheshakya Wijewardena, Manoj Kumar
• 2015 - Raghav RV <https://github.com/raghavrv>_, Wei Xue
• 2016 - Nelson Liu <https://nelsonliu.me>, YenChen Lin <https://yenchenlin.me/>

.. _Vlad Niculae: https://vene.ro/

...................

The NeuroDebian <https://neuro.debian.net>_ project providing Debian <https://www.debian.org/>_ packaging and contributions is supported by Dr. James V. Haxby <http://haxbylab.dartmouth.edu/>_ (Dartmouth College <https://pbs.dartmouth.edu/>_).

Donating to the project

If you have found scikit-learn to be useful in your work, research, or company, please consider making a donation to the project commensurate with your resources. There are several options for making donations:

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Donation Options:

• NumFOCUS: Donate via the NumFOCUS Donations Page <https://numfocus.org/donate-to-scikit-learn>_, scikit-learn's fiscal sponsor.

• GitHub Sponsors: Support the project directly through GitHub Sponsors <https://github.com/sponsors/scikit-learn>_.

• Benevity: If your company uses scikit-learn, you can also support the project through Benevity, a platform to manage employee donations. It is widely used by hundreds of Fortune 1000 companies to streamline and scale their social impact initiatives. If your company uses Benevity, you are able to make a donation with a company match as high as 100%. Our project ID is 433725 <https://causes.benevity.org/projects/433725>_.

All above donation options are managed by NumFOCUS <https://numfocus.org/>, a 501(c)(3) non-profit organization based in Austin, Texas, USA. The NumFOCUS board consists of SciPy community members <https://numfocus.org/board.html>. Contributions are tax-deductible to the extent allowed by law.

.. rubric:: Notes

Contributions support the maintenance of the project, including development, documentation, infrastructure and coding sprints.

Donations in kind

The following organizations provide non-financial contributions to the scikit-learn project.

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