1. Wrap-up
We have learned a lot in this course! Let's review what we have learned.
2. Introduction to FastAPI for Model Deployment
We began Chapter 1 by learning how to implement basic GET and POST requests with FastAPI. Next we learned how to load a pre-trained model and start the uvicorn server. We learned how to use Pydantic models to validate incoming data and format outgoing responses.
3. Integrating AI models
In Chapter 2 we tailored what we had already learned to AI use cases, learning how to handle and validate more structured input types, load a pre-trained model into the application, and return structured prediction results.
4. Securing and optimizing the API
In Chapter 3 we learned how to secure and optimize our APIs with key authentication, rate limiting, and asynchronous processing.
5. API versioning, monitoring and logging
Finally in Chapter 4 we learned best practices in API endpoint versioning and documentation, advanced input validation and error handling to support more complex nested structures like batch model inputs, and monitoring and logging to help troubleshoot and maintain our production APIs.
6. Congratulations!
FastAPI is a fantastic framework for building API applications that leverage AI. You have learned everything you need to build and deploy AI into production APIs with enterprise-class documentation, monitoring, and logging using the FastAPI framework. Congratulations!