Model drift is the gradual decline in an AI model's accuracy over time as the real world changes and no longer matches the data it was trained on. Customer behaviour, prices, or trends shift, and predictions grow less reliable. Because drift is inevitable, teams monitor performance and periodically retrain models on fresh data to keep them accurate after deployment.
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