Data Validation, Drift & Debugging

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

Practice