MLProvLab is a JupyterLab extension to track, manage, compare, and visualize the provenance of machine learning notebooks. The tool is designed to help data scientists and ML practitioners to automatically identify the relationships between data and models in ML scripts. It efficiently and automatically tracks the provenance metadata, including datasets and modules used. It provides users the facility to compare different runs of ML experiments, thereby ensuring a way to help them make their decisions. The tool helps researchers and data scientists to collect more information on their experimentation and interact with them.
Source Code: https://github.com/fusion-jena/MLProvLab/
Publication: Towards Tracking Provenance from Machine Learning Notebooks. Dominik Kerzel, Sheeba Samuel, Birgitta König-Ries, 13th International Conference on Knowledge Discovery and Information Retrieval (KDIR), 2021 (To appear)