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

Use cases for online model serving

Online model serving is essential when we need to see the impact of recent data on the model as soon as possible instead of waiting for a periodic batch update. In this section, we will discuss the following example cases where online serving is needed:

Recommending the nearest emergency center during a pandemic
Predicting the favorite soccer team in a tournament
Predicting the path of a hurricane or storm
Predicting the estimated delivery time of delivery trucks

Case 1 – recommending the nearest emergency center during a pandemic

Let’s assume a lot of people are becoming sick due to a pandemic, such as Covid, every day. However, there is a limited number of hospitals. We want to recommend the nearest, most convenient hospital to a patient, keeping the following things in mind: