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 over 160 machine learning models written in Julia and other languages. 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. The functionality of MLJ is distributed over a number of repositories illustrated in the dependency chart below. These repositories live at the JuliaAI umbrella organization.

Machine learning can be done using many languages, with Python and R being the most popular. But one language has been overlooked for some time--Julia. Julia isn't an obvious choice for machine learning simply because it's a new language that has only recently hit version 1.0. While Python is well-established, with a large community and many libraries, Julia simply doesn't have the community to shout about it. Right now Julia is used in various fields. From optimizing milk production in dairy farms to parallel supercomputing for astronomy, Julia has a wide range of applications.