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Building Data Science Applications with FastAPI

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

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801079211
Length 426 pages
Edition 1st Edition
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Author (1):
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François Voron François Voron
Author Profile Icon François Voron
François Voron
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Table of Contents (19) Chapters Close

Preface 1. Section 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 Injections in FastAPI 7. Section 2: Build and Deploy 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. Section 3: Build a Data Science API with Python and FastAPI
14. Chapter 11: Introduction to NumPy and pandas 15. Chapter 12: Training Machine Learning Models with scikit-learn 16. Chapter 13: Creating an Efficient Prediction API Endpoint with FastAPI 17. Chapter 14: Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV 18. Other Books You May Enjoy

Deploying a FastAPI application with Docker

Docker is a widely used technology for containerization. Containers are small, self-contained systems running on a computer. Each container contains all the files and configurations necessary for running a single application: a web server, a database engine, a data processing application, and so on. The main goal is to be able to run those applications without worrying about dependency and version conflicts that often happen when trying to install and configure them on the system.

Besides, Docker containers are designed to be portable and reproducible: to create a Docker container, you simply have to write a Dockerfile containing all the necessary instructions to build the small system, along with all the files and configuration you need. Those instructions are executed during a build, which results in a Docker image. This image is a package containing your small system, ready to use, that you can easily share on the internet through registries...

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