Deploy your first ML API with FastAPI
Turn a model into a production-ready API with validation, testing, and containerization.
Wrap the model behind a clear contract
Define request and response schemas with Pydantic so malformed input is rejected before it reaches your model. Load the model once at startup, not on every request.
Test the API like any other service
Use FastAPI’s test client to cover valid input, invalid input and model failures. Add a health endpoint so your orchestrator knows when the service is ready.
Containerize and ship
Pin dependencies, use a slim base image and run as a non-root user. Expose structured logs and basic latency metrics from day one so you can see how the service behaves under real load.
