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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

Architecting systems

No matter how you are working to build your software, it is always important to have a design in mind. This section will highlight the key considerations we must bear in mind when architecting ML systems.

Consider a scenario where you are contracted to organize the building of a house. We would not simply go out and hire a team of builders, buy all the supplies, hire all the equipment, and just tell everyone to start building. We would also not assume we knew exactly what the client who hired us wants without first speaking to them.

Instead, we would likely try to understand what the client wanted in detail, and then try to design the solution that would fit their requirements. We would potentially iterate this plan a few times with them and with appropriate experts who knew the details of pieces that fed into the overall design. Although we are not interested in building houses (or maybe you are, but there will not be any in this book!), we can still see the analogy...

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