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Machine Learning Engineering with Python

You're reading from   Machine Learning Engineering with Python Manage the production life cycle of machine learning models using MLOps with practical examples

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781801079259
Length 276 pages
Edition 1st Edition
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Author (1):
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Andrew P. McMahon Andrew P. McMahon
Author Profile Icon Andrew P. McMahon
Andrew P. McMahon
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Table of Contents (13) Chapters Close

Preface 1. Section 1: What Is ML Engineering?
2. Chapter 1: Introduction to ML Engineering FREE CHAPTER 3. Chapter 2: The Machine Learning Development Process 4. Section 2: ML Development and Deployment
5. Chapter 3: From Model to Model Factory 6. Chapter 4: Packaging Up 7. Chapter 5: Deployment Patterns and Tools 8. Chapter 6: Scaling Up 9. Section 3: End-to-End Examples
10. Chapter 7: Building an Example ML Microservice 11. Chapter 8: Building an Extract Transform Machine Learning Use Case 12. Other Books You May Enjoy

Section 3: End-to-End Examples

This section has the objective of working through some concrete examples in a way that pulls together all of the previous discussions in the book. These examples can be used as templates by readers of the book for future applications in their own work. The examples will have a particular focus on what decisions need to be made to select the best engineering approach, given what has been learned in the rest of the book. Once you have completed this section, you will have seen two examples of how to apply your new knowledge to archetypal machine learning engineering projects.

This section comprises the following chapters:

  • Chapter 7, Building an Example ML Microservice
  • Chapter 8, Building an Extract Transform Machine Learning Use Case
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