Automatically Tracking Metadata and Provenance of Machine Learning Experiments | Sebastian SchelterAutomatically Tracking Metadata and Provenance of Machine Learning Experiments Sebastian Schelter, Joos-Hendrik Böse, Johannes Kirschnick, Thoralf Klein, Stephan Seufert Abstract We present a lightweight system to extract, store and manage metadata and provenance information of common artifacts in machine learning (ML) experiments: datasets, models, predictions, evaluations and training runs. Our
http://www.vldb.org/pvldb/vol11/p1781-schelter.pdf
