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• Looper; An End-to-End ML Platform for Product Decisions

Looper; An End-to-End ML Platform for Product Decisions

• Episode 60 of the Stanford MLSys Seminar Series!
• Looper: an end-to-end ML platform for product decisions
• Speaker: Igor Markov

Abstract

Modern software systems and products increasingly rely on machine learning models to make data-driven decisions based on interactions with users, infrastructure and other systems. For broader adoption, this practice must (i) accommodate product engineers without ML backgrounds, (ii) support fine-grain product-metric evaluation and (iii) optimize for product goals. To address shortcomings of prior platforms, we introduce general principles for and the architecture of an ML platform, Looper, with simple APIs for decision-making and feedback collection.

Looper covers the end-to-end ML lifecycle from collecting training data and model training to deployment and inference, and extends support to personalization, causal evaluation with heterogenous treatment effects, and Bayesian tuning for product goals. During the 2021 production deployment Looper simultaneously hosted 440-1,000 ML models that made 4-6 million real-time decisions per second. We sum up experiences of platform adopters and describe their learning curve.

Bio

Igor L. Markov is a Research Scientist at Meta, previously an EECS professor at the University of Michigan. He received his Ph.D. in Computer Science from UCLA, is currently an IEEE Fellow and an ACM Distinguished Scientist. Prof. Markov researches computers that make computers. He has co-authored five books, four US patents, and over 200 refereed publications, some of which were honored by the best-paper awards at the Design Automation and Test in Europe Conference (DATE), the Int’l Symposium on Physical Design (ISPD), the Int’l Conference on Computer-Aided Design (ICCAD) and IEEE Trans. on Computer-Aided Design (TCAD). During the 2011 redesign of the ACM Computing Classification System, Prof. Markov led the effort on the Hardware tree. Prof. Markov is the recipient of a DAC Fellowship, an ACM SIGDA Outstanding New Faculty award, an NSF CAREER award, an IBM Partnership Award, a Microsoft A. Richard Newton Breakthrough Research Award, and the inaugural IEEE CEDA Early Career Award. He has served on the Executive Board of ACM SIGDA and Editorial Boards of several ACM and IEEE Transactions, Communications of the ACM and IEEE Design & Test..