Lale.jl is a Julia wrapper of lale Python library for semi-automated data science. Lale.jl offers scikit-learn compatible AutoML with algorithm selection and hyper-parameter tuning. Lale.jl provides a consistent high-level interface to existing AutoML optimizer backends such as Hyperopt, GridSearchCV, and SMAC. It has a standard search space specification with out of the box search space for 180 operators from scikit-learn, imblearn, AIF360, SnapML and more.
The talk will cover a demo of Lale (https://github.com/IBM/Lale.jl) usage including its highly flexible pipeline grammar to describe its support for more complicated workflows and their optimization. We will start with the basics followed by its typical usage and gradually disclose more features for AutoML application. Lale's simple and expressive pipeline grammar provides a great flexible toolkit for data scientists in Python and Julia which makes the creation and evaluation of different machine learning pipelines including complex ones, trivial.