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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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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

Red Hat OpenShift Data Science (RHODS)

In this section, you will explore the components that form the ML platform stack. The technology stack is a combination of Red Hat components, Red Hat partner components, and open source software. It’s called RHODS, and it’s Red Hat’s solution for running data science and ML workloads on OpenShift.

Running RHODS on OpenShift gives the freedom to build and deploy models on-premises or on any cloud. The open source version of RHODS is Open Data Hub (https://opendatahub.io). RHODS provides a subset of the components available in Open Data Hub but in a commercially supported way. The RHODS platform integrates well with technology partners to form a complete MLOps stack.

You will learn about the RHODS platform throughout this book. Let’s start by defining some of its building blocks:

  • Model development and tuning: RHODS provides out-of-the-box support for JupyterHub, a powerful and popular multi-user Jupyter...
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