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

Communicating with a SQL database with SQLAlchemy

To begin, we'll discuss how to work with a relational database using the SQLAlchemy library. SQLAlchemy has been around for years and is the most popular library in Python when you wish to work with SQL databases.

In this chapter, it's worth noting that we'll only consider the core part of the library, which only provides the tools to abstract communication with a SQL database. We won't consider the ORM part, as, in the next section, we'll focus on another ORM: Tortoise. As such, in this section, we'll pay very close attention to the SQL language.

Recently, async support has been added in version 1.4 but is not yet considered stable. That's why, for now, we'll combine it with the databases library by Encode, the same team behind Starlette, which provides an asynchronous connection layer for SQLAlchemy. In Figure 6.3, we have presented a schema for you to better visualize the interaction...