Rigorous quality assurance methodology ensuring model safety and performance. Covers functional testing, robustness evaluation, bias detection, and adversarial testing. Defines acceptance criteria, test data requirements, and validation protocols. Includes red teaming frameworks for identifying failure modes and safety issues. Addresses both pre-deployment validation and ongoing performance verification. Critical for high-stakes and regulated AI applications.
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