Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for a wide range of applications. Inspired by similar efforts in bioinformatics (BOSC) or statistics (useR), our aim is to build a forum for open source software in machine learning.

  • If you want more background about why open source software is important for machine learning, read our position paper about the need for open source software in machine learning.
  • If you have written machine learning software, consider adding it to the projects at mloss.org.
  • In case your machine learning software can be considered a useful, mature piece of work consider a submission to the JMLR track for machine learning open source software.


Our goal is to support a community creating a comprehensive open source machine learning environment. Ultimately, open source machine learning software should be able to compete with existing commercial closed source solutions. To this end, it is not enough to bring existing and freshly developed toolboxes and algorithmic implementations to people's attention. More importantly the MLOSS platform will facilitate collaborations with the goal of creating a set of tools that work with one another. Far from requiring integration into a single package, we believe that this kind of interoperability can also be achieved in a collaborative manner, which is especially suited to open source software development practices.

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We would like to acknowledge financial support from the PASCAL network of excellence, the Max-Planck Society and the Fraunhofer Society.