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.