Because model performance can degrade over time as | AIGP
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Because model performance can degrade over time as: Why does post-deployment AI governance call for continuous

AIGP Understanding How to Govern AI Deployment and Use Easy

Continuous monitoring and scheduled retraining exist because model performance drifts as post-deployment conditions change.

The question

Why does post-deployment AI governance call for continuous monitoring of a model together with a regular maintenance, update and retraining schedule?

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  1. Because a deployed model's behavior is permanently fixed and so never needs any further attention or review
    This is the opposite of reality; deployed models drift and require ongoing oversight.
  2. Because monitoring permanently removes all legal obligations tied to the deployed AI system
    Monitoring supports compliance but does not eliminate legal obligations.
  3. Because retraining is only ever performed once, immediately before the initial deployment
    Retraining is a recurring maintenance activity, not a one-time pre-launch step.
  4. Because model performance can degrade over time as real-world data and conditions shift after deployment ✓
    Live data and conditions drift, so ongoing monitoring and scheduled retraining keep performance from silently degrading.
The trap
Assuming a model validated at launch remains accurate indefinitely without ongoing monitoring.

How to remember it

Continuous monitoring and scheduled retraining exist because model performance drifts as post-deployment conditions change.

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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.