July 2026 focus: AutoML test suite enhancements for opendatahub-tests, with a security-first approach to credentials and a cleanup of test infrastructure. Delivered parametrized smoke tests for AutoML classification and regression, enabling flexible, single-method execution driven by task_type and environment-supplied S3 dataset keys. Hardened AWS credential management by sourcing credentials from transient Kubernetes Secrets (valueFrom) and removed plaintext keys and obsolete constants. Stabilized test fixtures and eliminated dead constants to improve reliability and maintainability. Expanded coverage with multiclass parametrization and guarded teardown to preserve workflow pods for debugging. Implemented pre-commit fixes and lint improvements to accelerate CI feedback and reduce churn. This combination reduces risk, speeds validation of the AutoML pipeline, and strengthens security and maintainability across the test suite.