A pure Julia machine learning framework.
Call for help. MLJ is getting attention but its small project team needs help to ensure its success. This depends crucially on:
Existing and developing ML algorithms implementing the MLJ model interface
Improvements to existing but poorly maintained Julia ML algorithms
MLJ is presently supported by a small Alan Turing Institute grant and is looking for new funding sources to grow the project.
MLJ aims to be a flexible framework for combining and tuning machine learning models, written in the high performance, rapid development, scientific programming language, Julia.
The MLJ project is partly inspired by MLR.
A list of models implementing the MLJ interface: MLJRegistry
In the Julia REPL:
]add MLJ add MLJModels
A docker image with installation instructions is also available.
Automated tuning of hyperparameters, including composite models with nested parameters. Tuning implemented as a wrapper, allowing composition with other meta-algorithms. ✔
Option to tune hyperparameters using gradient descent and automatic differentiation (for learning algorithms written in Julia).
Data agnostic: Train models on any data supported by the Tables.jl interface. ✔
Intuitive syntax for building arbitrarily complicated learning networks .✔
Learning networks can be exported as self-contained composite models ✔, but common networks (e.g., linear pipelines, stacks) come ready to plug-and-play.
Performant parallel implementation of large homogeneous ensembles of arbitrary models (e.g., random forests). ✔
Task interface matches machine learning problem to available models. ✔
Benchmarking a battery of assorted models for a given task.
Automated estimates of cpu and memory requirements for given task/model.
The ScikitLearn SVM models will not work under Julia 1.0.3 but do work under Julia 1.1 due to Issue #29208
When MLJRegistry is updated with new models you may need to force a new precompilation of MLJ to make new models available.
Predecessors of the current package are AnalyticalEngine.jl and Orchestra.jl, and Koala.jl. Work continued as a research study group at the University of Warwick, beginning with a review of existing ML Modules that were available in Julia at the time (in-depth, overview).
Further work culminated in the first MLJ proof-of-concept
about 24 hours ago