Model Monitoring and Drift

Explore how to identify and manage data drift affecting machine learning models after deployment. Learn methods to detect shifts in input features and target distributions, quantify drift severity, and decide when retraining is needed. Gain insights into building monitoring pipelines, setting alerts, handling challenges like lack of labels, and maintaining model reliability in real-world environments.