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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

Summary

In this chapter, you have seen how Red Hat ODS integrates with third-party software to further simplify your MLOps journey. OpenShift makes it a breeze to use the data versioning capabilities of the Pachyderm software.

You have seen how Red Hat OpenShift Pipelines enables your data science team to automate the model training workflow by providing drag-and-drop capabilities and using the same code you have used to manually train the model.

You will use the core pipeline capabilities in the next few chapters, where you'll further automate your workflow. In the next chapter, you will use what you have learned about creating pipelines to deploy the model as a service.

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