OpML '20 - Time Travel and Provenance for Machine Learning Pipelines

Time Travel and Provenance for Machine Learning Pipelines Alexandru A. Ormenisan, KTH - Royal Institute of Technology; Moritz Meister, Fabio Buso, and Robin Andersson, Logical Clocks AB; Seif Haridi and Jim Dowling, KTH - Royal Institute of Technology Machine learning pipelines have become the defacto paradigm for productionizing machine learning applications as they clearly abstract the processing steps involved in transforming raw data into engineered features that are then used to train models. In this