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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

Optimizing cost

When it comes to managing an OpenShift cluster, it’s not just about making sure your applications run smoothly; it’s also about keeping a close eye on your cloud infrastructure costs. OpenShift is incredibly powerful, but if you’re not careful, it can lead to unnecessary overspending. In this guide, we’ll dive into some practical strategies to help you fine-tune your OpenShift cluster, so you can strike that perfect balance between having the resources you need and keeping your budget under control. From optimizing how you allocate resources to scaling your cluster intelligently, these practices will empower you to make the most of your Kubernetes setup without breaking the bank:

  • Rightsize resources: Take a closer look at the resource requirements of your applications running in Pods. Adjust the allocated CPU and memory to match the actual needs of each application. Avoid overallocating resources, which can lead to unnecessary costs...
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