When monitoring detects drift or performance degradation, models don't self-heal. Someone must retrain. Continuous learning automates the "detect degradation → retrain → validate → redeploy" loop. This closes the MLOps circle: yesterday's CI/CD deploys models, today's operations monitor them, and today's continuous learning retrains them. MLA-C01 tests "when and how to trigger automated retraining".
Core concept: Drift trigger (data/model performance falls below threshold) → Retrain pipeline auto-starts → New model registers → Quality gate evaluation → If approved, auto-deploy