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MLJ.jl

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What MLJ.jl is

MLJ.jl is a free, open-source Julia package for general, composable machine learning. It provides a common interface for selecting, loading, evaluating, tuning, comparing, and composing models. A searchable registry stores model metadata and loads models when needed. The ecosystem supports more than 180 models written in Julia and other languages, including many scikit-learn models, as well as neural-network models built with Flux.jl. Pipelines, stacks, and learning networks combine model components; pipeline parameters can be tuned together, while learning networks can train several models and become standalone model types. Grid and RandomSearch provide hyperparameter optimization, and workflows support probabilistic predictions. Data can come through Tables.jl-compatible sources, including CSV.jl, DataFrames/Tables.jl tables, RDatasets, and OpenML. Supported workflows may use CPU threading or multiprocessing, with serial CPU processing as the default. MLJ is installed with Julia’s package manager using Pkg.add("MLJ").

Who it suits

It suits Julia users who need a common workflow for selecting, composing, evaluating, and tuning machine-learning models. It is also relevant to contributors with prior Julia and machine-learning experience.

What it does well

  • Free under the MIT Expat License
  • Supports more than 180 machine-learning models
  • Pipelines and learning networks combine models
  • Grid and RandomSearch support tuning
  • Accepts several tabular data sources

What to know first

  • Linux is the only listed platform
  • Model packages generally require separate installation
  • MLflow integration is under reassessment
  • Serial CPU processing is the default

Verdict

MLJ.jl brings model selection, composition, evaluation, and tuning into one Julia interface. Its broad model coverage and composable workflows suit users comfortable adding provider packages and working in Julia.

Facts from MLJ.jl's own pages

Acceleration
Supported workflows can use CPU threading or multiprocessing; serial CPU processing is the default. · juliaai.github.io, 12 Oct 2026
Additional packages
Users generally need to add model-providing packages to access model-specific code, and MLJ's interactive load command can prompt for a provider. · juliaai.github.io, 12 Oct 2026
Audience
The project welcomes contributors who can demonstrate prior experience in Julia and machine learning. · juliaml.ai, 11 Oct 2026
Bayesian analysis
Yes · github.com, 23 Sept 2026
Composition
MLJ supports pipelines, stacks, and learning networks for composing models. · juliaai.github.io, 12 Oct 2026
Core functions
MLJ provides a common interface and meta-algorithms for selecting, tuning, evaluating, composing, and comparing machine learning models. · juliaai.github.io, 12 Oct 2026
Data import formats
CSV (CSV.jl), DataFrames/Tables.jl tables, RDatasets, OpenML · github.com, 23 Sept 2026
Data support
MLJ works with data supported by the Tables.jl interface when Tables.istable(X) is true. · juliaai.github.io, 12 Oct 2026
Evaluation and tuning
MLJ provides syntax to tune and evaluate composite models and supports probabilistic predictions. · juliaai.github.io, 12 Oct 2026
Free plan
Yes · github.com, 23 Sept 2026
Installation
MLJ is installed with Julia's package manager using Pkg.add("MLJ"). · juliaai.github.io, 12 Oct 2026
Integrations
MLJ's ecosystem includes OpenML.jl integration for the OpenML data science exchange platform. · juliaml.ai, 12 Oct 2026
Learning networks
Learning networks can train multiple models together and can be exported as standalone model types. · juliaml.ai, 11 Oct 2026
License
MLJ is open source and released under the MIT license. · juliaml.ai, 12 Oct 2026
Model coverage
The documentation says MLJ supports over 180 machine learning models written in Julia and other languages, including many scikit-learn models. · juliaai.github.io, 12 Oct 2026
Model registry
Its model registry contains metadata for models that users can search and load on demand. · juliaml.ai, 11 Oct 2026
Model support
The MLJ ecosystem includes support for neural network models built with Flux.jl. · juliaml.ai, 11 Oct 2026
Pipelines
Pipelines can combine model components, and their parameters can be tuned together. · juliaml.ai, 11 Oct 2026
Project history
The MLJ project began at the Alan Turing Institute in 2019 as a Tools, Practices and Systems project. · juliaml.ai, 12 Oct 2026
Purpose
MLJ.jl is a Julia package for general, composable machine learning at scale. · juliaml.ai, 12 Oct 2026
Regression models
Yes · github.com, 23 Sept 2026
Scripting support
Yes · github.com, 23 Sept 2026
Security
The official About page describes the license, project history, and governance, but does not state security or compliance certifications. · juliaml.ai, 11 Oct 2026
Support
The documentation directs users to Julia Discourse, GitHub issues, or the #mlj channel in Julia Slack for questions and reports. · juliaai.github.io, 12 Oct 2026

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