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

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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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21. Other Books You May Enjoy

Introducing TensorFlow Serving

In this section, we will provide a high-level introduction to the TensorFlow architecture and its key concepts. TensorFlow is designed to provide a high-performance production serving environment for serving machine learning models. TensorFlow provides default integration with TensorFlow models but it can be extended to other models as well, such as scikit-learn. To learn more about integrating other libraries with TensorFlow Serving, please go to https://www.tensorflow.org/tfx/guide/non_tf. TensorFlow provides support for easily deploying new models by keeping the architecture and the APIs the same, making provisioning the versioning support of the models in production easier.

To understand the architecture of TensorFlow, you first need to understand the following key concepts.

Servable

A servable is the name of the abstraction object that is used by the client for computation or inference. For example, if a client makes a prediction request...