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MLOps with Red Hat OpenShift

You're reading from  MLOps with Red Hat OpenShift

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781805120230
Pages 238 pages
Edition 1st Edition
Languages
Authors (2):
Ross Brigoli Ross Brigoli
Profile icon Ross Brigoli
Faisal Masood Faisal Masood
Profile icon Faisal Masood
View More author details

Table of Contents (13) Chapters

Preface Part 1: Introduction
Chapter 1: Introduction to MLOps and OpenShift Part 2: Provisioning and Configuration
Chapter 2: Provisioning an MLOps Platform in the Cloud Chapter 3: Building Machine Learning Models with OpenShift Part 3: Operating ML Workloads
Chapter 4: Managing a Model Training Workflow Chapter 5: Deploying ML Models as a Service Chapter 6: Operating ML Workloads Chapter 7: Building a Face Detector Using the Red Hat ML Platform Index Other Books You May Enjoy

Optimizing cost for your ML platform

In this section, you will learn how to use different OpenShift capabilities with Red Hat Data Science to optimize the cost for your platform. While we will not dive deep into this topic, we will provide you with some basic concepts to continue optimizing your platform resources.

When you run any software on the Red Hat OpenShift platform, such as a Jupyter notebook, build pipelines, and model serving, all of it runs as containers on the platform. These containers run on the machines or worker nodes, which could be a VM in a cloud platform such as Amazon EC2. Let’s see how OpenShift provisions machines to run containers for your MLOps needs.

Machine management in OpenShift

Machine management is OpenShift’s capability to work with the cloud or on-premises infrastructure providers, such as Amazon Web Services (AWS) or VMware (VMW), and to provision and scale the machines for your workloads. OpenShift adapts to changing workloads...

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