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AI & Machine Learning · Foundational

Model Evaluation

Measuring a trained model's performance, robustness and fitness for purpose before and after deployment.

Definition

Evaluation uses held-out data and task-appropriate metrics, and increasingly includes fairness, robustness, safety and cost assessments. For generative AI it combines automated metrics, LLM-as-a-judge scoring and human review. Production evaluation continues through monitoring and A/B testing.

Exam tip

Pick metrics that align with business cost of errors, not just the default.

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