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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 Machine Learning Models with OpenShift

In the previous chapter, you installed and configured OpenShift to power your machine learning (ML) project life cycle. In this chapter, you will configure the platform components required for model development. This chapter will equip you with what is available on the OpenShift platform for building ML models and how your team can leverage it. Please ensure that you have completed the setup mentioned in the previous chapter before starting this chapter.

This is the first stage of the ML development life cycle, which we presented in Chapter 2. In this chapter, you will see how easy it is for you and your team to start building with the technology provided by Red Hat OpenShift for Data Science (RHODS).

We will cover the following topics:

  • Using Jupyter Notebooks in OpenShift
  • Using ML frameworks in OpenShift
  • Using GPU acceleration for model training
  • Building custom notebooks
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