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Most Teams Find Out Too Late."}],["$","p",null,{"className":"mt-4 text-lg text-muted-foreground leading-relaxed font-medium","children":"A model that was accurate at deployment will not remain accurate indefinitely. The question is not whether it will decay, but when and how fast — and whether you have the infrastructure to detect it."}]]}]}],["$","article",null,{"className":"mx-auto max-w-[800px] px-6 py-12 md:py-16","children":[["$","div",null,{"className":"bg-card border border-border rounded-xl p-8 md:p-12 shadow-sm space-y-6","children":[["$","p","0",{"className":"text-base text-foreground leading-[1.85] font-medium","children":"A model that was accurate at deployment will not remain accurate indefinitely. The relationship between inputs and outputs that the model learned from historical data reflects the state of the world at the time that data was collected. The world changes. Customer behavior shifts. Economic conditions move. Competitors change their pricing. The source systems that feed the model produce data in subtly different formats after a schema migration. Each of these changes degrades model performance. The question is not whether a deployed model will decay, but when and how fast — and whether the organization has the monitoring infrastructure to detect the decay before it produces consequential decision errors."}],["$","p","1",{"className":"text-base text-foreground leading-[1.85] font-medium","children":"Data drift is the change in the statistical distribution of model inputs that occurs without any change in the model itself. When the average transaction value in the training data was two hundred dollars and the current transaction value has shifted to three hundred dollars, a model that learned its patterns from the training distribution is making predictions based on input values that it has limited experience with. 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