You're reading from Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python

Product type Paperback

Published in Jul 2023

Publisher Packt

ISBN-13 9781837632749

Length 422 pages

Edition 2nd Edition

Languages

Python

Tools

FastAPI

Concepts

Data Science

Author (1):

François Voron

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Table of Contents (21) Chapters

Preface

1. Part 1: Introduction to Python and FastAPI

2. Chapter 1: Python Development Environment Setup FREE CHAPTER

3. Chapter 2: Python Programming Specificities

4. Chapter 3: Developing a RESTful API with FastAPI

5. Chapter 4: Managing Pydantic Data Models in FastAPI

6. Chapter 5: Dependency Injection in FastAPI

7. Part 2: Building and Deploying a Complete Web Backend with FastAPI

8. Chapter 6: Databases and Asynchronous ORMs

9. Chapter 7: Managing Authentication and Security in FastAPI

10. Chapter 8: Defining WebSockets for Two-Way Interactive Communication in FastAPI

11. Chapter 9: Testing an API Asynchronously with pytest and HTTPX

12. Chapter 10: Deploying a FastAPI Project

13. Part 3: Building Resilient and Distributed Data Science Systems with FastAPI

14. Chapter 11: Introduction to Data Science in Python

15. Chapter 12: Creating an Efficient Prediction API Endpoint with FastAPI

16. Chapter 13: Implementing a Real-Time Object Detection System Using WebSockets with FastAPI

17. Chapter 14: Creating a Distributed Text-to-Image AI System Using the Stable Diffusion Model

18. Chapter 15: Monitoring the Health and Performance of a Data Science System

19. Index

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Part 3: Building Resilient and Distributed Data Science Systems with FastAPI

This part will introduce you to the basic concepts of data science and machine learning, as well as the most popular Python tools and libraries for those tasks. We’ll see how to integrate those tools into a FastAPI backend and how to build a distributed system to perform resource-intensive tasks in a scalable way.

This section comprises the following chapters:

Chapter 11, Introduction to Data Science in Python
Chapter 12, Creating an Efficient Prediction API Endpoint with FastAPI
Chapter 13, Implementing a Real-Time Object Detection System Using WebSockets with FastAPI
Chapter 14, Creating a Distributed Text-to-Image AI System Using the Stable Diffusion Model
Chapter 15, Monitoring the Health and Performance of a Data Science System

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You're reading from Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python

Table of Contents (21) Chapters

Part 3: Building Resilient and Distributed Data Science Systems with FastAPI

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You're reading from Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python

Table of Contents (21) Chapters

Part 3: Building Resilient and Distributed Data Science Systems with FastAPI

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