The Kale EKF component simplifies the use of Kubeflow, giving data scientists the tools they need to orchestrate end-to-end ML workflows. Kale provides both an SDK and a GUI in the form of a JupyterLab extension.
The SDK can be used to orchestrate workflows from any repository of Python code. The aim of the Kale SDK is to allow you to write plain Python code and then be able to convert it into fully reproducible Kubeflow pipelines without making any changes to the original source code.
The JupyterLab extension provides a convenient GUI for workflow orchestration from within Jupyter Notebooks.
Kale enables you to run hyperparameter tuning jobs, thanks to its integration with Katib, serve models by spawning KFServing InferenceServices, and execute AutoML configurations using AutoSklearn. Finally, Kale allows you to explore the lineage of the trained models by logging MLMD Artifacts and make the whole process reproducible using Rok snapshots.