HTTPS ML Validator

The HTTPS ML Validator tests secure endpoints that host ML models behind TLS. It focuses on data scientists, ML engineers, backend developers, and QA teams who need confidence that their model endpoints accept correct inputs and return well-formed outputs. The tool does not replace integration tests but provides targeted checks for data contracts, payload fidelity, and response consistency, enabling faster deployment cycles.
Conceptual workflow: supply endpoint_url, http_method, headers, and a sample payload; optionally provide an expected_status and a JSON Schema. The validator sends the request, enforces TLS, and validates whether the actual status and headers align with expectations. If a JSON body is returned, the tool validates it against the schema and flags any missing fields, wrong types, or structural deviations. Latency is captured to aid performance tuning.
Why it matters: ML teams ship models to production through HTTP(S) APIs; regressions in data contracts or changed inputs can cause subtle failures. This tool helps catch those issues early, with clear error_details to accelerate debugging. Core features include endpoint test orchestration, optional batch runs, JSON Schema validation, and structured result export that supports CI/CD workflows.
Use cases include validating a retrained model endpoint after data schema changes, verifying feature vectors in POST payloads, and ensuring consistent error handling across environments. The tool’s focus on ML contexts provides a practical differentiator compared with generic API checkers.