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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

Building custom notebooks

Though RHODS comes with a few in-built notebooks that you can use, you may require a different library version and/or dependency, or you may want to add your organization certificates to the notebook. The point is there can be many reasons why the provided notebooks may require some tuning.

In this section, you will learn how to tune the existing notebook images, import third-party notebook images, and create your custom notebook images.

RHODS allows you to bring notebook images into the platform, either by importing an existing container image from a registry such as DockerHub, Quay, or any other container registry, or by customizing an existing notebook image. Let’s look at how to create custom notebook images and import them into RHODS.

Creating a custom notebook image

Creating custom notebook images follows a standard container image build process. This involves creating a Dockerfile that describes how the container image is to be built...

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