FastAPI's type annotations, Pydantic models, and lifespan context manager solve three common ML serving problems: loading expensive artifacts before requests start, validating inputs without manual checks, and returning useful errors when dependencies aren't ready. This tutorial walks through the patterns used in the flight disruption prediction API.
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.
