Book Image

Building Data Science Applications with FastAPI

By : François Voron
5 (1)
Book Image

Building Data Science Applications with FastAPI

5 (1)
By: François Voron

Overview of this book

FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you’ll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you’ll cover best practices relating to testing and deployment to run a high-quality and robust application. You’ll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you’ll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you’ll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you’ll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI.
Table of Contents (19 chapters)
1
Section 1: Introduction to Python and FastAPI
7
Section 2: Build and Deploy a Complete Web Backend with FastAPI
13
Section 3: Build a Data Science API with Python and FastAPI

Implementing an efficient prediction endpoint

Now that we have a way to save and load our machine learning models, it's time to use them in a FastAPI project. As you'll see, the implementation shouldn't be too much of a surprise if you've followed this book. The main part of the implementation is the class dependency, which will take care of loading the model and making predictions. If you need a refresher on class dependencies, check out Chapter 5, Dependency Injections in FastAPI.

Let's go! Our example will be based on the newgroups model we dumped in the previous section. We'll start by showing you how to implement the class dependency, which will take care of loading and making predictions:

chapter13_prediction_endpoint.py

class PredictionInput(BaseModel):
    text: str
class PredictionOutput(BaseModel):
    category: str
class NewsgroupsModel:
    model: Optional[Pipeline]
 ...