An ML inference API has a constraint that a standard REST API does not: its most expensive resource is not a database connection or a network socket — it is the model artifact itself.
Learn how to use Python's `Generic[T]` protocol with Pydantic v2's `BaseModel` to create a single, typed response envelope that propagates concrete type information through to OpenAPI schema generation, provides consistent operational metadata across all endpoints without repetition, and enforces field-level constraints on both inbound and outbound data.
VitalCheck is a production-deployed REST API that delivers real-time health risk assessments across ten medical and wellness domains. Built with Python, FastAPI, and a suite of machine learning models, it provides a unified interface for clinical and biometric predictions — from diabetes and stroke risk to MRI-based brain tumor classification.
