Model and data conditions can shift over time | AIGP
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Model and data conditions can shift over time: What is the primary reason continuous monitoring is needed

AIGP Understanding How to Govern AI Development Easy

Post-deployment conditions drift, so continuous monitoring detects degradation early and triggers maintenance.

The question

A governance standard requires continuous monitoring of a deployed AI system alongside a regular schedule for maintenance, updates, and retraining. What is the primary reason continuous monitoring is needed after deployment?

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  1. Regulators require that all source code be recompiled on a fixed monthly calendar after any launch.
    Plausible-sounding but there is no such recompilation mandate; monitoring targets ongoing behavior, not code builds.
  2. Model and data conditions can shift over time, so ongoing checks catch degradation and drift early. ✓
    Continuous monitoring detects drift and performance degradation that emerge after deployment.
  3. It permanently freezes the model so that its behavior can never change once it enters production use.
    Plausible-sounding but monitoring observes change over time; it does not freeze the model in place.
  4. It replaces the need for any pre-release testing because live traffic reveals every possible defect.
    Plausible-sounding but monitoring complements pre-release testing rather than replacing it entirely.
The trap
Thinking monitoring freezes the model or replaces pre-release testing rather than detecting drift over time.

How to remember it

Post-deployment conditions drift, so continuous monitoring detects degradation early and triggers maintenance.

How many of these would you get right?

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