TL;DR — ML Monitoring

Design an ML monitoring system that tracks model performance in production, detects data drift and concept drift, and alerts teams when model quality degrades. The system monitors input feature distributions, prediction distributions, and ground-truth performance metrics continuously. Key features: Ingest model predictions, features, and ground truth labels. Detect feature drift using statistical tests (PSI, KS, chi-squared).