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ML health monitor: taking the pulse of machine learning algorithms in production
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Paper Abstract

Bringing the research advances in Machine Learning (ML) to production is necessary for businesses to gain value from ML. A key challenge of production ML is the monitoring and management of real-time prediction quality. This is complicated by the variability of live production data, the absence of real-time labels and the non-determinism posed by ML techniques themselves. We define ML Health as the real time assessment of ML prediction quality and present an approach to monitoring and improving ML Health. Specifically, a complete solution to monitor and manage ML Health within a realistic full production ML lifecycle. We describe a number of ML Health techniques and assess their efficacy via publicly available datasets. Our solution handles production realities such as scale, heterogeneity and distributed runtimes. We present what we believe is the first solution to production ML Health explored at both an empirical and complete system implementation level.

Paper Details

Date Published: 12 September 2019
PDF: 12 pages
Proc. SPIE 11139, Applications of Machine Learning, 111390R (12 September 2019); doi: 10.1117/12.2529598
Show Author Affiliations
Sindhu Ghanta, ParallelM, Inc. (United States)
Sriram Subramanian, ParallelM, Inc. (United States)
Lior Khermosh, ParallelM, Inc. (United States)
Swaminathan Sundararaman, ParallelM, Inc. (United States)
Harshil Shah, ParallelM, Inc. (United States)
Yakov Goldberg, ParallelM, Inc. (United States)
Drew Roselli, ParallelM, Inc. (United States)
Nisha Talagala, ParallelM, Inc. (United States)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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