You're reading from Machine Learning Model Serving Patterns and Best Practices A definitive guide to deploying, monitoring, and providing accessibility to ML models in production

Product type Paperback

Published in Dec 2022

Publisher Packt

ISBN-13 9781803249902

Length 336 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Machine Learning

Author (1):

Md Johirul Islam

View More author details

Table of Contents (22) Chapters

Preface

1. Part 1:Introduction to Model Serving

2. Chapter 1: Introducing Model Serving FREE CHAPTER

3. Chapter 2: Introducing Model Serving Patterns

4. Part 2:Patterns and Best Practices of Model Serving

5. Chapter 3: Stateless Model Serving

6. Chapter 4: Continuous Model Evaluation

7. Chapter 5: Keyed Prediction

8. Chapter 6: Batch Model Serving

9. Chapter 7: Online Learning Model Serving

10. Chapter 8: Two-Phase Model Serving

11. Chapter 9: Pipeline Pattern Model Serving

12. Chapter 10: Ensemble Model Serving Pattern

13. Chapter 11: Business Logic Pattern

14. Part 3:Introduction to Tools for Model Serving

15. Chapter 12: Exploring TensorFlow Serving

16. Chapter 13: Using Ray Serve

17. Chapter 14: Using BentoML

18. Part 4:Exploring Cloud Solutions

19. Chapter 15: Serving ML Models using a Fully Managed AWS Sagemaker Cloud Solution

20. Index

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Technical requirements

In this chapter, we will mostly use the same libraries that we have used in the previous chapters. You should have Postman or another REST API client installed to be able to send API calls and see the responses. All the code for this chapter is provided at https://github.com/PacktPublishing/Machine-Learning-Model-Serving-Patterns-and-Best-Practices/tree/main/Chapter%2012.

If you get ModuleNotFoundError while trying to import any library, then you should install that module using the pip3 install <module_name> command.

You also need to install Docker for this chapter. Please make sure that you install Docker from https://www.docker.com/. Docker is the easiest and most recommended system for using TensorFlow Serving, as per the official documentation at https://www.tensorflow.org/tfx/serving/docker.