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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

Designing our forecasting service

The requirements in the Understanding the forecasting problem section are the definitions of the targets we need to hit, but they are not the method for getting there. Drawing on our understanding of design and architecture from Chapter 5, Deployment Patterns and Tools, we can start building out our design.

First, we should confirm what kind of design we should be working on. Since we need dynamic requests, it makes sense that we follow the microservice architecture discussed in Chapter 5, Deployment Patterns and Tools. This will allow us to build a service that has the sole focus of retrieving the right model from our model store and performing the requested inference. The prediction service should therefore have interfaces available between the dashboard and the model store.

Furthermore, since a user may want to work with a few different store combinations in any one session and maybe switch back and forth between the forecasts of these...

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