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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

Selecting the tools

For this example, and pretty much for whenever we have an ETML problem, our main considerations boil down to a few simple things, which we will cover in the following sections.

Interfaces

When we execute the extract and load parts of ETML, we need to consider how to interface with the systems that store our data. It is important that whichever database or data technology we are extracting from, we use the appropriate tools to extract at whatever scale and pace we need. In this example, our interfacing can be taken care of by the AWS boto3 library and the S3 Application Programming Interface (API) it surfaces.

The following table shows the pros and cons of using this option:

Figure 8.3 – Pros and cons of using the AWS CLI and boto3 for interfacing with our data sources

In the next section, we will consider the decisions we must make around the scalability of our modeling approach. This is very important when working with...

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