Best practices for operating large-scale recommender systems

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 th