Summary
Congratulations! You’re now able to build a fast and efficient REST API to serve your machine learning models. Thanks to Joblib, you learned how to dump a trained scikit-learn estimator into a file that’s easy to load and use inside your application. We also saw an approach to caching prediction results using Joblib. Finally, we discussed how FastAPI handles synchronous operations by sending them to a separate thread to prevent blocking. While this was a bit technical, it’s important to bear this aspect in mind when dealing with blocking I/O operations.
We’re near the end of our FastAPI journey. Before letting you build awesome data science applications by yourself, we will provide three more chapters to push this a bit further and study more complex use cases. We’ll start with an application that can perform real-time object detection, thanks to WebSockets and a computer vision model.
