Open Source for
common good
Creating impact together
Develop, maintain, and sustain open source
projects and communities in data science.
We believe technology serves as a catalyst for societal progress, adopting various models, mostly open source, sometimes closed source, fully managed or on-premises solutions, each playing its role in a complex system.
Our approach and commitment is to provide a fully open source, reversible, and transparent contribution, catering to the needs of the data science and machine learning community.
Projects we support & help maintain
Leading machine learning library.
Simple and efficient tools for predictive data analysis.
Accessible to everybody, and reusable in various contexts.
Prepping tables for machine learning.
- scikit-learn compatible
- Pandas and Polars dataframes inputs and outputs
- Work on heterogeneous types (numeric, categorical, dates, text, missing values...
Alternative and more secure way of persisting ML models, and tools to document models with their performance and limitations.
The most scikit-learn compatible library related to responsible AI. It has tools to monitor certain fairness related issues with the AI systems, and other tools to mitigate them.
Software platform implementing and extending the standards of the Semantic Web. It allows to create, manipulate, parse, serialize, query, reason and validate RDF data.
Imbalanced-learn offers a number of techniques commonly used in datasets showing strong between-class imbalance.
Joblib is a set of tools to provide lightweight pipelining in Python. It is optimized to be fast and robust on large data in particular and has specific optimizations for numpy arrays.
Have specific needs on any of these libraries?
Make a Request
Want to join the list of scikit-learn corporate sponsors and partners?
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