RecSysOps: Best practices for operating a large-scale recommender system

Abstract

Ensuring the health of a modern large-scale recommendation system is a very challenging problem. To address this, we need to put in place proper logging, sophisticated exploration policies, develop ML-interpretability tools or even train new ML models to predict/detect issues of the main production model. In this talk, we shine a light on this less-discussed but important area and share some of the best practices, called RecSysOps, that we've learned while operating our increasingly complex recommender systems at Netflix. RecSysOps is a set of best practices for identifying issues and gaps as well as diagnosing and resolving them in a large-scale machine-learned recommender system. RecSysOps helped us to 1) reduce production issues and 2) increase recommendation quality by identifying areas of improvement and 3) make it possible to bring new innovations faster to our members by enabling us to spend more of our time on new innovations and less on debugging and firefighting issues.