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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

Containerizing and deploying to Kubernetes

When we introduced Docker in Chapter 5, Deployment Patterns and Tools, we showed how you can use it to encapsulate your code and then run it across many different platforms consistently.

Here we will do this again, but with the idea in mind that we don’t just want to run the application as a singleton on a different piece of infrastructure, we actually want to allow for many different replicas of the microservice to be running simultaneously with requests being routed effectively by a load balancer. This means that we can take what works and make it work at almost arbitrarily large scales.

We will do this by executing several steps:

  1. Containerize the application using Docker.
  2. Push this Docker container to Docker Hub to act as our container storage location (you could use another container management solution like AWS Elastic Container Registry or similar solutions on another cloud provider for this step).
  3. ...
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