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MLOps with Red Hat OpenShift

You're reading from   MLOps with Red Hat OpenShift A cloud-native approach to machine learning operations

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781805120230
Length 238 pages
Edition 1st Edition
Tools
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Authors (2):
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Ross Brigoli Ross Brigoli
Author Profile Icon Ross Brigoli
Ross Brigoli
Faisal Masood Faisal Masood
Author Profile Icon Faisal Masood
Faisal Masood
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Toc

Table of Contents (13) Chapters Close

Preface 1. Part 1: Introduction FREE CHAPTER
2. Chapter 1: Introduction to MLOps and OpenShift 3. Part 2: Provisioning and Configuration
4. Chapter 2: Provisioning an MLOps Platform in the Cloud 5. Chapter 3: Building Machine Learning Models with OpenShift 6. Part 3: Operating ML Workloads
7. Chapter 4: Managing a Model Training Workflow 8. Chapter 5: Deploying ML Models as a Service 9. Chapter 6: Operating ML Workloads 10. Chapter 7: Building a Face Detector Using the Red Hat ML Platform 11. Index 12. Other Books You May Enjoy

Introduction to MLOps and OpenShift

If you have chosen to read this book, chances are that you have a background in the machine learning (ML) domain. The primary purpose of this book is to show you how Red Hat OpenShift provides the basis for developing, deploying, and monitoring your models in production. In addition, you will learn about different components of the OpenShift ecosystem and how you can weave them together to build a path toward automating the life cycle of your ML project. You will also learn how to leverage Red Hat OpenShift Data Science and its partner components.

Finally, you will see how the approaches presented in this book can help your organization scale its ML initiatives through MLOps practices.

This first chapter focuses on giving you the basic definitions of the concepts and the technologies involved in the Red Hat OpenShift ecosystem for machine learning.

This chapter will cover the following topics:

  • What is machine learning operations...
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