# MLJ: A Julia package for composable machine learning

arXiv: https://arxiv.org/abs/2007.12285 · HTML: https://mlsanity.com/paper/2007.12285/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [How to avoid machine learning pitfalls: a guide for academic researchers](https://mlsanity.com/paper/2108.02497/) (2021)
- [GeCo: Quality Counterfactual Explanations in Real Time](https://mlsanity.com/paper/2101.01292/) (2021)
- [Explaining Black-Box Models through Counterfactuals](https://mlsanity.com/paper/2308.07198/) (2023)
- [Designing Machine Learning Toolboxes: Concepts, Principles and Patterns](https://mlsanity.com/paper/2101.04938/) (2021)
- [OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language](https://mlsanity.com/paper/2211.04550/) (2022)
- [EBIC.JL -- an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia](https://mlsanity.com/paper/2105.01196/) (2021)
- [A Review of Open Source Software Tools for Time Series Analysis](https://mlsanity.com/paper/2203.05195/) (2022)
- [A knowledge-driven AutoML architecture](https://mlsanity.com/paper/2311.17124/) (2023)
- [mlr3torch: A Deep Learning Framework in R based on mlr3 and torch](https://mlsanity.com/paper/2604.18152/) (2026)

## Abstract (arXiv)

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.