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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

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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