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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

Adding custom data validation with Pydantic

Up to now, we've seen how to apply basic validation to our models, through the Field arguments or the custom types provided by Pydantic. In a real-world project, though, you'll probably need to add your own custom validation logic for your specific case. Pydantic allows this by defining validators, which are methods on the model that can be applied at a field level or an object level.

Applying validation at a field level

This is the most common case: have a validation rule for a single field. To define it in Pydantic, we'll just have to write a static method on our model and decorate it with the validator decorator. As a reminder, decorators are syntactic sugar, allowing the wrapping of a function or a class with common logic, without compromising readability.

The following example checks a birth date by verifying that the person is not more than 120 years old:

chapter4_custom_validation_01.py

from datetime import...
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