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

Using TensorFlow Serving to serve models

In this section, we will use TensorFlow Serving to serve models. First, we will use the recommended mechanism of using TensorFlow with Docker. The official page presenting this example is https://www.tensorflow.org/tfx/serving/docker.

TensorFlow Serving with Docker

Make sure Docker is installed on your platform. Follow the link provided in the Technical requirements section to start Docker. Now, let’s work through the following steps to serve our dummy example model:

First of all, start Docker and make sure it is running. You can verify whether Docker is running by running the docker –version command in your operating system’s terminal. It should give you an output similar to the following:
```
→  TF_SERVE docker --version
```
```
Docker version 20.10.11, build dea9396
```
Now, let’s pull the latest TensorFlow Docker image using the docker pull tensorflow/serving command. You should see the following...

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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

Table of Contents (22) Chapters

Using TensorFlow Serving to serve models

TensorFlow Serving with Docker

Authors (1)

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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

Table of Contents (22) Chapters

Using TensorFlow Serving to serve models

TensorFlow Serving with Docker

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