Model drift is the phenomenon where ML models become less accurate over time as the data they process evolves. AI/ML engineers are responsible for detecting drift and retraining models to maintain effectiveness. For example, a customer churn model may drift as customer behavior changes, requiring periodic retraining with new data.
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