How do you implement automated testing for AI and machine learning systems?

ML testing goes beyond traditional software testing to validate data, models, and pipelines. Testing levels: (1) Data tests: validate training data quality (no nulls in required fields, feature distributions within expected ranges, no data leakage from future to past), validate data splits (stratified splits maintain class distribution), and detect training-serving skew (training data distribution matches production distribution). (2) Model tests: unit tests for feature engineering functions, model performance tests against acceptance thresholds (accuracy, F1, AUC above minimums per class), fairness tests (performance parity across demographic groups), robustness tests (model handles edge cases, adversarial inputs, out-of-distribution inputs), and regression tests (new model version doesn't degrade on previously passing test cases). (3) Pipeline tests: end-to-end pipeline execution tests, data flow validation between pipeline stages, error handling and recovery tests, and idempotency tests (re-running produces same results). (4) Integration tests: model serving endpoint responds correctly, inference latency meets SLA, model handles concurrent requests, and A/B testing infrastructure routes correctly. Implementation: use Great Expectations for data validation, pytest with custom fixtures for model testing, implement golden dataset testing (curated examples that must always pass), and maintain test data separate from training data. CI/CD integration: run data validation on every data update, run model tests on every training run, implement model quality gates (models must pass all tests before deployment), and track test metrics over time. Monitoring as testing: production monitoring extends testing into the live environment. Track prediction distribution, feature distribution, model latency, and error rates, alerting when metrics deviate from expected ranges. Challenges: ML test failures can be non-deterministic (model training involves randomness), performance thresholds are subjective, and test data maintenance requires ongoing effort.