Video by H2O.ai via YouTube
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.
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AI Fundamentals Glossary | H2O.ai University
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