Model Drift, Risk & Interpretability: When AI Models Change Over Time

A model that was accurate last year can quietly become unreliable this year. Drift is why, and interpretability is how you catch it. Terms covered: - Drift: production data starts to look different from training data. Consumer behavior shifts, conditions change, and the ground beneath the model moves. - Model Drift: the resulting degradation in performance, which leads to poor predictions being acted on as if they were still reliable - Risk Management: identifying and mitigating bias, security exposure, regulatory risk and unexpected edge case behavior - ML Interpretability: showing why a model made a decision, so it can be explained, audited and challenged Drift tells you when a model is changing. Risk management tells you what to protect against. Interpretability tells you what the model is actually doing. — AI Fundamentals Glossary | H2O.ai University Full playlist: https://www.youtube.com/playlist?list=PLAWKlIKS-66E Free courses and certifications: https://h2o.ai/university #ModelDrift #ResponsibleAI #MLOps #AIGlossary #H2Oai