MLJ: A Julia package for composable machine learning

TL;DR

MLJ is an open-source Julia package that offers a unified interface for machine learning models, enabling flexible composition, tuning, and evaluation, with advantages over multi-language solutions.

Contribution

It introduces a novel, flexible framework for model composition and evaluation in Julia, enhancing usability and integration compared to existing multi-language tools.

Findings

Provides a unified interface for models in Julia and other languages

Enables flexible model composition and tuning

Highlights Julia's advantages over multi-language alternatives

Abstract

MLJ (Machine Learing in Julia) is an open source software package providing a common interface for interacting with machine learning models written in Julia and other languages. It provides tools and meta-algorithms for selecting, tuning, evaluating, composing and comparing those models, with a focus on flexible model composition. In this design overview we detail chief novelties of the framework, together with the clear benefits of Julia over the dominant multi-language alternatives.