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Machine Learning Engineering  with Python

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

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781837631964
Length 462 pages
Edition 2nd 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 (12) Chapters Close

Preface 1. Introduction to ML Engineering 2. The Machine Learning Development Process FREE CHAPTER 3. From Model to Model Factory 4. Packaging Up 5. Deployment Patterns and Tools 6. Scaling Up 7. Deep Learning, Generative AI, and LLMOps 8. Building an Example ML Microservice 9. Building an Extract, Transform, Machine Learning Use Case 10. Other Books You May Enjoy
11. Index

Selecting the tools

For this example, and pretty much whenever we have an ETML problem, our main considerations boil down to a few simple things, namely the selection of the interfaces we need to build, the tools we need to perform the transformation and modeling at the scale we require, and how we orchestrate all of the pieces together. The next few sections will cover each of these in turn.

Interfaces and storage

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 extract from, we use the appropriate tools to extract at whatever scale and pace we need. In this example, we can use S3 on AWS for our storage; our interfacing can be taken care of by the AWS boto3 library and the AWS CLI. Note that we could have selected a few other approaches, some of which are listed in Table 9.2 along with their pros and cons.

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