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 BentoML

BentoML is a popular tool for serving ML models. It provides support for deploying models created using almost all the popular libraries. Throughout this section, we will discuss how to get started with BentoML and how to use it for serving, along with some key concepts.

We will discuss the following concepts that are needed to use BentoML:

Preparing models
Services and APIs
Bento

Let’s discuss each concept in detail.

Preparing models

A trained ML model cannot be directly served using BentoML because BentoML needs to convert all the models into a common format so that it can extend support to any models from any ML library. The model needs to be saved using the BentoML API. BentoML provides the save_model API for almost all the popular ML libraries. For example, if you develop an ML model using the scikit-learn library, then you need to use the bentoml.sklearn.save_model(...) API to save the model for serving using BentoML...

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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

Introducing BentoML

Preparing models

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

Introducing BentoML

Preparing models

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Authors (1)

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