Testing & Validation
The systematic process of evaluating AI models against benchmarks, edge cases, and stress conditions to ensure they meet performance, safety, and compliance criteria.
Definition
Encompasses unit tests for individual components, integration tests for data pipelines, regression tests against historical data, edge-case scenarios (adversarial, rare events), and stress tests on scalability and security. Validation includes statistical performance metrics, fairness audits, and compliance checks. Governance enforces that no model reaches production without passing a comprehensive test-and-validation checklist approved by independent reviewers.
Real-World Example
A credit-risk model’s testing suite includes: hold-out validation on recent loan data; stress tests with simulated economic downturn scenarios; bias tests across income and demographic groups; and API load tests. Only after passing all stages does the model receive final sign-off for deployment.
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