MLJ.jl
The facts below are what the maker publishes about MLJ.jl, checked and dated. Our editors have not written about it yet.
The facts
Each one read from MLJ.jl's own pages, with the date it was checked.
What it does
- Acceleration
- Supported workflows can use CPU threading or multiprocessing; serial CPU processing is the default.checked 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.checked 12 Oct 2026
- Audience
- The project welcomes contributors who can demonstrate prior experience in Julia and machine learning.checked 11 Oct 2026
- Bayesian analysis
- Yeschecked 23 Sep 2026
- Composition
- MLJ supports pipelines, stacks, and learning networks for composing models.checked 12 Oct 2026
- Core functions
- MLJ provides a common interface and meta-algorithms for selecting, tuning, evaluating, composing, and comparing machine learning models.checked 12 Oct 2026
- Data import formats
- CSV (CSV.jl), DataFrames/Tables.jl tables, RDatasets, OpenMLchecked 23 Sep 2026
- Data support
- MLJ works with data supported by the Tables.jl interface when Tables.istable(X) is true.checked 12 Oct 2026
- Evaluation and tuning
- MLJ provides syntax to tune and evaluate composite models and supports probabilistic predictions.checked 12 Oct 2026
- Installation
- MLJ is installed with Julia's package manager using Pkg.add("MLJ").checked 12 Oct 2026
- Integrations
- MLJ's ecosystem includes OpenML.jl integration for the OpenML data science exchange platform.checked 12 Oct 2026
- Learning networks
- Learning networks can train multiple models together and can be exported as standalone model types.checked 11 Oct 2026
- License
- MLJ is open source and released under the MIT license.checked 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.checked 12 Oct 2026
- Model registry
- Its model registry contains metadata for models that users can search and load on demand.checked 11 Oct 2026
- Model support
- The MLJ ecosystem includes support for neural network models built with Flux.jl.checked 11 Oct 2026
- Pipelines
- Pipelines can combine model components, and their parameters can be tuned together.checked 11 Oct 2026
- Project history
- The MLJ project began at the Alan Turing Institute in 2019 as a Tools, Practices and Systems project.checked 12 Oct 2026
- Purpose
- MLJ.jl is a Julia package for general, composable machine learning at scale.checked 12 Oct 2026
- Regression models
- Yeschecked 23 Sep 2026
- Scripting support
- Yeschecked 23 Sep 2026
- Security
- The official About page describes the license, project history, and governance, but does not state security or compliance certifications.checked 11 Oct 2026
- Support
- The documentation directs users to Julia Discourse, GitHub issues, or the #mlj channel in Julia Slack for questions and reports.checked 12 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
