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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

Summary

In this chapter, we walked through an example of how to take the tools and techniques from the first seven chapters of this book and apply them together to solve a realistic business problem. We discussed in detail how the need for a dynamically triggered forecasting algorithm can lead very quickly to a design that requires several small services to interact seamlessly. In particular, we created a design with components responsible for handling events, training models, storing models, and performing predictions. We then walked through how we would choose our toolset to build to this design in a real-world scenario, by considering things such as appropriateness for the task at hand, as well as likely developer familiarity. Finally, we carefully defined the key pieces of code that would be required to build the solution to solve the problem repeatedly and robustly.

In the next, and final, chapter, we will build out an example of a batch ML process. We will name the pattern...

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