MLflow2PROV: Extracting Provenance from Machine Learning Experiments

Abstract

Supporting iterative and explorative workflows for developing machine learning (ML) models, ML experiment management systems (ML EMSs), such as MLflow, are increasingly used to simplify the structured collection and management of ML artifacts, such as ML models, metadata, and code. However, EMSs typically suffer from limited provenance capabilities. As a consequence, it is hard to analyze provenance information and gain knowledge that can be used to improve both ML models and their development workflows. We propose a W3C-PROV-compliant provenance model capturing ML experiment activities that originate from Git and MLflow usage. Moreover, we present the tool MLflow2PROV that extracts provenance graphs according to our model, enabling querying, analyzing, and further processing of collected provenance information.

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