API endpoints
Project phase: Deployment and Maintenance
You build the service layer that exposes your trained model to the outside world: at minimum a prediction endpoint and a health/status endpoint, with a clear specification of each endpoint's request and response format. You add input validation and error handling that protects the model from malformed input, and document how to run the service and how the model is versioned so every prediction can be traced back to the exact model that produced it.
Starting Points
Key Points
- Plan & Design: You can explain your architectural choices (e.g., why you chose a specific framework like FastAPI or Flask) and follow a clear implementation plan.
- Functionality & Validation: The service starts without errors, handles malformed input (out-of-range or wrong types) with sensible error messages, and returns predictable results.
- End-user Impact: You have implemented security and reliability measures, such as input validation and error handling, to protect the service and the user.
- Traceability: The model artefact is loaded and versioned in a way that makes predictions reproducible.
- Usability: A reviewer can run your service using your instructions (e.g., a Dockerfile or a requirements.txt) and test it immediately using your documented examples.
- Documentation: The API specification (like Swagger/OpenAPI) is clear and complete.