Wendelin Exanalytics Libre

WENDELIN combines Scikit Learn machine learning and NEO distributed storage for out-of-core data analytics in python

The idea of Wendelin is based on an informal discussion between Jean-Paul Smets and Alexandre Gramfort about big data.

Wendelin was then launched by Nexedi at MariaDB conference in 2014 (Santa Clara). Its goal is to create the best open source, big data engine based on Numpy python technologies such as scikit-learn machine learning library and gather a wide community of contributors of new data analytics algorithms.

Wendelin is now being developped jointly by Nexedi and Télcom Paritech.

Wendelin = scikit-learn + NEO

Wendelin combines the performance of scikit-learn machine learning with NEO distributed storage in order to provide out-of-core processing of large data sets with scikit-learn.

Main applications

Wendelin current main applications are industrial big data and video processing. Any industrial problem of prediction can be handled with Wendelin: mechanical health prediction, intrusion prediction, power prediction, etc. In addition, thanks to other Numpy based libraries such as OpenCV or Pandas, Wedelin can be applied to other fields such as video processing or finance.

Doing Business

Wendelin project encourages business applications.

Besides support and consulting business that are typical of open source, Wendelin can be extended by adding open source or proprietary component to fit a given vertical market in big data. The Wendelin project is looking for industrial partners willing to adapt Wendelin to more vertical markets and reinvest part of their revenue into Wendelin core and in particular into scikit-learn.

Thanks to this business model, Wendelin long term R&D can be sustainable. R&D financing does not depend on Venture Capital and does not bear the risk of a hostile takeover that other open source technologies may be facing.