Failure Mode Analysis for ML models is a systematic study of how, when, and why models fail — categorizing failure types, identifying common patterns, and developing strategies to mitigate or prevent each failure mode in production deployment.

ML Failure Mode Categories

• Data Failures: Out-of-distribution inputs, data quality issues, concept drift.
• Model Failures: Overconfident wrong predictions, poor calibration, catastrophic forgetting.
• Integration Failures: Incorrect preprocessing, stale models, feature mismatch between training and serving.
• Adversarial Failures: Intentional or accidental inputs that cause incorrect predictions.

Why It Matters

• Proactive Mitigation: Understanding failure modes enables designing defenses before deployment.
• Risk Assessment: Quantify the probability and impact of each failure mode for risk management.
• FMEA Analogy: Similar to FMEA (Failure Mode and Effects Analysis) used in semiconductor manufacturing quality.

Failure Mode Analysis is cataloging everything that can go wrong — systematically understanding ML failure modes to design robust production systems.