Production ML systems fail silently. Input distributions shift, labels go stale, and features become undefined. Systematic validation, range checks, distribution shift detection, label consistency, turns silent failures into loud, catchable errors.
Learning Objectives
→Validate feature arrays against a schema of expected ranges
→Detect population shift with a simple chi-squared statistic
→Identify label leakage by checking feature-label temporal ordering
→Compute the Population Stability Index (PSI) to quantify distribution drift