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Machine Learning Model Serving Patterns and Best Practices

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

Why subscribe?

21. Other Books You May Enjoy

Online Learning Model Serving

In online model serving, we want the impact on the model of recent data to be visible as soon as the data is available. In some cases, during online training, retraining of the model may happen during the serving of each response. This is done to ensure that the model is up to date with newly arrived data. In this chapter, we will talk about the online model serving pattern. We will introduce you to online model serving, the advantages and disadvantages of online model serving, and techniques for online model serving.

In this chapter, we will cover the following topics:

Introducing online model serving
Use cases for online model serving
Challenges in online model serving
Implementing online model serving

The rest of the chapter is locked

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Authors (1)

Md Johirul Islam

Md Johirul Islam

Md Johirul Islam is a Data Scientist and Machine Learning Researcher at AWS. He has a PhD in Computer Science and is also an adjunct professor at Purdue University. His expertise are focused on designing explainable, maintainable and robust data science pipeline applying the software design principles and helping organizations deploy Machine Learning models into production at Scale.

See other products by Md Johirul Islam

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