Continuous monitoring with a scheduled retraining | AIGP
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Continuous monitoring with a scheduled retraining: Which ongoing governance practice most directly addresses

AIGP Understanding How to Govern AI Deployment and Use Medium

Drift-driven accuracy loss is addressed by continuous monitoring plus a scheduled cadence of maintenance, updates and retraining.

The question

Six months after launch, a demand-forecasting model's accuracy is slipping because customer behavior has shifted away from the patterns in its training data. Which ongoing governance practice most directly addresses this degradation?

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  1. A one-time pre-deployment impact assessment of the selected model
    A valid earlier step, but a single pre-launch assessment cannot correct accuracy that drifts months after go-live.
  2. An external communication plan describing the outage to customers
    Sometimes useful, yet communicating about degradation does not restore the model's accuracy against shifting data.
  3. A licensing review confirming the contractually permitted deployment scope
    A legitimate due-diligence task, but contract scope is unrelated to correcting drift-induced performance decline.
  4. Continuous monitoring with a scheduled retraining and update cadence
    Detecting drift-driven accuracy loss and scheduling retraining is exactly continuous monitoring paired with a maintenance cadence.
The trap
Believing a single pre-deployment assessment substitutes for ongoing monitoring and scheduled retraining once data drifts.

How to remember it

Drift-driven accuracy loss is addressed by continuous monitoring plus a scheduled cadence of maintenance, updates and retraining.

How many of these would you get right?

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More Understanding How to Govern AI Deployment and Use questions

Part of the Certsqill AIGP question bank · Understanding How to Govern AI Deployment and Use · Every answer, right and wrong, comes with its own explanation.