Machine Learning in Production - Roman Kazinnik | Stanford MLSys #66

Episode 66 of the Stanford MLSys Seminar Series! Machine Learning in Production: Review of Empirical Solutions Speaker: Roman Kazinnik Abstract: Taking stock of ML Infra problems with potential to benefit from systematic analysis. ML currently requires running large amounts experiments to compensate for the lack of analysis. Modern AI infrastructure (major clouds) is efficient in creating, training, and deploying thousands of model. At the same time, improving production models performance, accurate estimation of models performance in production, web data relevance, risk mitigation - these are ad hoc and experiment-driven processes. Analytical analysis for Production [distributed, large-scale, rapidly changing environment] ML can help to direct and hopefully replace the empirical and manual processes. Bio: Roman Kazinnik is working at Meta on the AI Platform team. He is an experienced computer programmer passionate about empirical and theoretical work. He created algorithms for Ads serving, deep Earth oil