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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

Installing partner software on RedHat ODS

In order to complete our MLOps platform, we will need to install additional tools to OpenShift to complement the features of ODS. Several tools are considered partner software of the ODS platform. These software products are listed in the ODS console and can be viewed by clicking the Explore menu item in the ODS console, as shown in Figure 2.34:

Figure 2.34 – ODS console showing partner software

Figure 2.34 – ODS console showing partner software

One of the things that you will need to complete the MLOps platform is data versioning, and you will need data lineage too. We will use Pachyderm for this need.

Pachyderm is a powerful OSS tool designed to manage data in modern data pipelines. It serves as a data versioning and lineage system, enabling efficient tracking and control of data changes throughout the data processing life cycle.

With Pachyderm, you can easily keep track of modifications made to your data, similar to how version control systems ...

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