Conduct a focused local validation using labeled samples | AIGP
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Conduct a focused local validation using labeled samples: Which evaluation is most defensible?

AIGP Understanding How to Govern AI Deployment and Use Hard

Validate locally because the vendor benchmark does not represent the plant’s operating conditions.

The question

A manufacturer’s vendor reports excellent benchmark accuracy for defect detection. The local plant uses different camera angles, materials, and lighting, and only a small labeled sample is available. Deployment requires evidence about local performance before production use. Which evaluation is most defensible?

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  1. Deploy while collecting local labels for later review.
    Delayed validation leaves the production decision unsupported under the stated requirement for local evidence before use.
  2. Conduct a focused local validation using labeled samples. ✓
    Local validation tests the system under actual plant conditions while acknowledging limited sample size and the benchmark’s narrower scope.
  3. Accept the vendor benchmark as sufficient evidence.
    The benchmark’s scope may not match local cameras, materials, or lighting, so it cannot establish production performance here.
  4. Replace the vendor model with an internally trained model immediately.
    Building another model does not itself provide local validation and may introduce additional untested performance and operational risks.
The trap
Ask whether evaluation conditions represent the actual deployment environment, users, data, and operational constraints.

How to remember it

Validate locally because the vendor benchmark does not represent the plant’s operating conditions.

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