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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 Jul 2023
Publisher Packt
ISBN-13 9781837632749
Length 422 pages
Edition 2nd 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 (21) Chapters Close

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 20. Other Books You May Enjoy

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...

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