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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
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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 a model training pipeline

Red Hat OpenShift pipelines automate training and deployment workflows. They are based on the Kubeflow pipeline domain-specific language (DSL) and backed by the Tekton engine. In this section, you will build a pipeline from the notebook you created earlier. In the next chapters, you will add more stages to this pipeline.

Installing Red Hat OpenShift Pipelines

This is a familiar process where you log in to OpenShift, select the right operator, and perform an install. Follow the next steps to install the pipeline operator:

  1. Log in to the OpenShift console and search for OpenShift Pipelines from OperatorHub, as shown in Figure 4.9. Click on the Red Hat OpenShift Pipelines tile:
Figure 4.9 – OpenShift Pipelines operator

Figure 4.9 – OpenShift Pipelines operator

  1. Using all the default options, click on the Install button, as shown in Figure 4.10:

Note

The version of the operator may have already changed by the time you are reading...

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