A Machine Learning Framework for Julia
MLJ (Machine Learning in Julia) is a toolbox written in Julia providing a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing about 200 machine learning models written in Julia and other languages.
New to MLJ? Start here.
Integrating an existing machine learning model into the MLJ framework? Start here.
Wanting to contribute? Start here.
PhD and Postdoc opportunies See here.
MLJ was initially created as a Tools, Practices and Systems project at the Alan Turing Institute in 2019. Current funding is provided by a New Zealand Strategic Science Investment Fund awarded to the University of Auckland.
MLJ has been developed with the support of the following organizations:
The MLJ Universe
The functionality of MLJ is distributed over several repositories illustrated in the dependency chart below. These repositories live at the JuliaAI umbrella organization.
Dependency chart for MLJ repositories. Repositories with dashed connections do not currently exist but are planned/proposed.
Core design: A. Blaom, F. Kiraly, S. Vollmer
Lead contributor: A. Blaom
Active maintainers: A. Blaom, S. Okon, T. Lienart, D. Aluthge