Model Governance: Reducing the Anarchy of Production ML


Vinay Sridhar, Sriram Subramanian, Dulcardo Arteaga, Swaminathan Sundararaman, Drew Roselli, and Nisha Talagala, ParallelM


As the influence of machine learning grows over decisions in businesses and human life, so grows the need for Model Governance. In this paper, we motivate the need for, define the problem of, and propose a solution for Model Governance in production ML. We show that through our approach one can meaningfully track and understand the who, where, what, when, and how an ML prediction came to be. To the best of our knowledge, this is the first work providing a comprehensive framework for production Model Governance, building upon previous work in developer-focused Model Management.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

Presentation Audio

@inproceedings {216047,
author = {Vinay Sridhar and Sriram Subramanian and Dulcardo Arteaga and Swaminathan Sundararaman and Drew Roselli and Nisha Talagala},
title = {Model Governance: Reducing the Anarchy of Production {ML}},
booktitle = {2018 {USENIX} Annual Technical Conference ({USENIX} {ATC} 18)},
year = {2018},
isbn = {978-1-931971-44-7},
address = {Boston, MA},
pages = {351--358},
url = {},
publisher = {{USENIX} Association},