Model Drift
Model Drift (or model decay) is the degradation of an AI model's predictive performance in production over time, caused by changes in the statistical properties of real-world input data relative to the training data.
Frequently Asked Questions
What is concept drift vs. data drift?▼
Data drift (covariate shift) is when input distributions change (e.g. users search for new terms). Concept drift is when the relationship between inputs and targets changes (e.g. consumer preferences shift, making old classification rules obsolete).
How is model drift resolved?▼
By continuous monitoring in MLOps, setting up anomaly alerts on input distributions, and automatically triggers retraining pipelines on fresh production data.
Quick Facts
- CategoryModel Operations
- Key ApplicationMLOps model tracking, prediction monitoring, and automated retraining pipelines.
Coverage Trend12 Weeks
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Model Drift Media Coverage & Intelligence
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