The Playbook to Monitor Your Model’s Performance in Production

Model monitoring has become increasingly important as machine learning infrastructure matures. However, there is no foolproof playbook for measuring model performance in every situation. Performance analysis can be complex, especially when ground truth is not immediately available or biased. In such cases, proxy metrics and statistical distances can be used to monitor prediction drift. Additionally, measuring business outcomes alongside model metrics provides a comprehensive understanding of how models affect customers' experiences with the product.