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Model drift is the gradual loss of a production model's accuracy as real-world data shifts away from what it learned during training. This guide breaks down the three primary types of drift (data, concept, and label), what causes them, and how to detect drift early using performance monitoring and statistical tests. You'll also learn the prevention practices that keep retraining efficient and models accurate over time.
Sama provides ML Professionals and AI team Leads with an indispensable solution for Computer Vision data labeling.
