Lale.jl is a Julia wrapper of Python’s Lale library for semi-automated data science. Lale makes it easy to automatically select algorithms and tune hyperparameters of pipelines that are compatible with scikit-learn, in a type-safe fashion.
Lale provides easy pipeline composition using combinators and visualization of the pipeline for ease of understanding. Performing combined algorithm selection and hyperparameter tuning (CASH) on the given pipeline is just 3 lines of code. Here is an example notebook demonstrating some basic functionality of Lale.jl.
Lale.jl has a good coverage of machine learning operators from scikit-learn and also operators from the imbalanced-learn Python package. We are working on adding more operators.
Would be happy to get feedback.