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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

Designing an ETML solution

The requirements clearly point us to a solution that takes in some data and augments it with ML inference, before outputting the data to a target location. Any design we come up with must encapsulate these steps.

The key elements our design has to cover can be articulated by expanding out what we wrote down in Figure 8.1; this is shown in the following diagram:

Figure 8.2 – High-level design for the ETML clustering system

In the next section, we will look at some potential tools we can use to solve this problem, given everything we have learned in previous chapters.

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