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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

By : Voron
4.7 (16)
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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

4.7 (16)
By: 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)
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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

Working with Pydantic objects

When developing API endpoints with FastAPI, you'll likely get a lot of Pydantic model instances to handle. It's then up to you to implement the logic to make a link between those objects and your services, such as your database or your machine learning (ML) model. Fortunately, Pydantic provides methods to make this very easy. We'll review common use cases that will be useful for you during development.

Converting an object into a dictionary

This is probably the action you'll perform the most on a Pydantic object: convert it to a raw dictionary that'll be easy to send to another API or use in a database, for example. You just have to call the dict method on the object instance.

The following example reuses the Person and Address models we saw in the Standard field types section of this chapter:

chapter4_working_pydantic_objects_01.py

person = Person(
    first_name="John",
  ...
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