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AI

Model drift

Definition

Degradation in an automated system's output over time as the underlying model is updated, or as the real-world inputs move away from what it was tuned on. It arrives quietly, without an error message.

A classifier tuned in January against a provider's model can behave differently in June after a silent version update. Nothing breaks; the output is simply worse, and nobody notices until a downstream number moves.

The defence is a small held-out set of examples with known correct answers, run on a schedule, with an alert on the failure rate. It is a day of work and it is the difference between finding out from a test and finding out from a customer.

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