A sudden shift in the incoming data distribution—such as seasonal changes in customer behavior—is known as data drift. CPMAI defines model drift as “degradation in a model’s performance over time as the underlying data distribution changes,” implying that the root cause is the data itself shifting. Recognizing data drift is the first step in adapting both data pipelines and models to maintain performance .

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