Chapter 13: Creating an Efficient Prediction API Endpoint with FastAPI
In the previous chapters, we introduced the most common data science techniques and libraries largely used in the Python community. Thanks to those tools, we can now build machine learning models that can make efficient predictions and classify data. Of course, we now have to think about a convenient interface so that we can take advantage of their intelligence. This way, microservices or frontend applications can ask our model to make predictions to improve the user experience or business operations.
In this chapter, we'll learn how to do that with FastAPI. As we've seen throughout this book, FastAPI allows us to implement very efficient REST APIs with clear and lightweight syntax. In this chapter, you'll learn how to do this as efficiently as possible so that it can serve thousands of prediction requests. To help us with this task, we'll introduce another library, Joblib, that provides tools...
