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AI Model Deployment & MLOps › Model Drift Detection

What is Model Drift Detection

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Answer

Model drift detection is the process of identifying when a deployed model's performance drops because the real world data has changed since it was trained.
Real-world example A retailer detects model drift when customer buying patterns shift after a major holiday season, causing the old recommendation model to become less accurate.

Common follow-ups: What is AI Model Deployment & MLOps, How is Model Drift Detection evaluated in practice, What tools are commonly used for Model Drift Detection

AI Model Deployment & MLOps topics: Introduction to MLOps Model Versioning CI/CD for Machine Learning
All 7 Model Drift Detection questions