failure mode analysis

**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.