Data Cloud Video Hub

14. Monitoring Model's drift

This segment focuses on model monitoring within AI, specifically addressing model drift and its significance. It explains how drift occurs when there are shifts in applicant profiles that affect a model’s approval and decline decisions. The importance of real-time drift monitoring is emphasized, showcasing how it can help identify changes early, allowing for timely retraining of models to maintain accuracy. Users can easily activate drift tracking in Data Cloud, and the presentation includes demonstrations of monitoring both input and prediction drift, highlighting the need for immediate action when significant changes are detected.