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Machine Learning on Kubernetes

You're reading from Machine Learning on Kubernetes A practical handbook for building and using a complete open source machine learning platform on Kubernetes

Product type Paperback

Published in Jun 2022

Publisher Packt

ISBN-13 9781803241807

Length 384 pages

Edition 1st Edition

Languages

Python

Tools

Kubernetes

Concepts

Machine Learning

Authors (2):

Ross Brigoli

Faisal Masood

View More author details

Table of Contents (16) Chapters

Preface

1. Part 1: The Challenges of Adopting ML and Understanding MLOps (What and Why)

2. Chapter 1: Challenges in Machine Learning FREE CHAPTER

3. Chapter 2: Understanding MLOps

4. Chapter 3: Exploring Kubernetes

5. Part 2: The Building Blocks of an MLOps Platform and How to Build One on Kubernetes

6. Chapter 4: The Anatomy of a Machine Learning Platform

7. Chapter 5: Data Engineering

8. Chapter 6: Machine Learning Engineering

9. Chapter 7: Model Deployment and Automation

10. Part 3: How to Use the MLOps Platform and Build a Full End-to-End Project Using the New Platform

11. Chapter 8: Building a Complete ML Project Using the Platform

12. Chapter 9: Building Your Data Pipeline

13. Chapter 10: Building, Deploying, and Monitoring Your Model

14. Chapter 11: Machine Learning on Kubernetes

15. Other Books You May Enjoy

Summary

In this chapter, you have reviewed some basic Kubernetes concepts and gone through the Operator ecosystem in the Kubernetes universe. If you want to learn more about Kubernetes, The Kubernetes Workshop by Packt is a good place to start.

You have installed the tooling required to set up a local Kubernetes cluster. You have seen the instructions to do it in other environments such as Linux, macOS, and Windows. You have set up a VM on Google Cloud in case you do not want to use your local computer for the exercises. You have configured OLM to manage Operators on your Kubernetes cluster. These technologies will form the infrastructure foundation of our ML platform, which you will start to shape up in the next chapter.

The rest of the chapter is locked

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Authors (2)

Faisal Masood

Faisal Masood

Faisal Masood is a cloud transformation architect at AWS. Faisal's focus is to assist customers in refining and executing strategic business goals. Faisal main interests are evolutionary architectures, software development, ML lifecycle, CD and IaC. Faisal has over two decades of experience in software architecture and development.

See other products by Faisal Masood

Brigoli

Brigoli

Ross Brigoli is a consulting architect at Red Hat, where he focuses on designing and delivering solutions around microservices architecture, DevOps, and MLOps with Red Hat OpenShift for various industries. He has two decades of experience in software development and architecture.

See other products by Brigoli

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