Open Source for
common good

Creating impact together

Develop, maintain, and sustain open source
projects and communities in data science.

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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.

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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.

  • Built for scikit-learn, Python
  • Robust to dirty data
  • Easy learning on pandas dataframes

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.

Survival and competing risk analysis, as a scikit-learn compatible library.

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
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