Openlayer ML Testing is a machine learning testing tool designed to evaluate ML models before and after deployment. It integrates into the ML development lifecycle, running tests automatically on every code push and tracking changes across models, prompts, and data versions. The tool supports testing across multiple data modalities: - Tabular - NLP - Vision - Multimodal systems Core testing capabilities include: - Behavioral testing: Probes edge cases, adversarial inputs, and safety-critical scenarios to verify model behavior under unexpected conditions. - Drift detection: Monitors upstream features and downstream predictions for distribution shifts over time. - Fairness and bias auditing: Evaluates model performance across demographic slices and sensitive attributes to identify disparate impacts. - Test coverage tracking: Identifies which classes, segments, and failure modes are covered by each test suite and highlights gaps. - Regression testing: Compares new model checkpoints against previous versions to detect unintended performance regressions. - CI/CD integration: Automates test execution on every pull request or scheduled job, enabling consistent quality gates. The tool also supports evaluation types ranging from basic null checks and drift tests to LLM-as-a-judge style evaluations. It is recognized in the Gartner Market Guide for AI Evaluation and Observability.