Book Image

Building Data Science Applications with FastAPI - Second Edition

By : François Voron
3 (1)
Book Image

Building Data Science Applications with FastAPI - Second Edition

3 (1)
By: François Voron

Overview of this book

Building Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects – a real-time object detection system and a text-to-image generation platform using Stable Diffusion. The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications. Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios. By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements.
Table of Contents (21 chapters)
1
Part 1: Introduction to Python and FastAPI
7
Part 2: Building and Deploying a Complete Web Backend with FastAPI
13
Part 3: Building Resilient and Distributed Data Science Systems with FastAPI

Communicating with a MongoDB database using Motor

As we mentioned at the beginning of this chapter, working with a document-oriented database, such as MongoDB, is quite different from a relational database. First and foremost, you don’t need to configure a schema upfront: it follows the structure of the data that you insert into it. In the case of FastAPI, it makes our life slightly easier since we only have to work with Pydantic models. However, there are some subtleties around the document identifiers that we need to take into account. We’ll review this next.

To begin, we’ll install Motor, which is a library that is used to communicate asynchronously with MongoDB and is officially supported by the MongoDB organization. Run the following command:

(venv) $ pip install motor

Once you’ve done this, we can start working!

Creating models that are compatible with MongoDB ID

As we mentioned in the introduction to this section, there are some difficulties...