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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

Configuring Pachyderm

Let’s start by configuring Pachyderm. Pachyderm is a platform that assists data scientists in creating complete ML workflows covering all the stages from data ingestion and model training up to deploying into production. Think of it as a version control system (VCS) for your model development workflow.

In traditional software engineering, you may use Git to version control your code. In ML projects, you need to version control your data, and you want a reproducible flow for training your model. Pachyderm provides such capabilities for you. You will see how Red Hat OpenShift enables you to use Pachyderm. Refer to Chapter 3 for instructions on installing the Pachyderm operator.

Follow these steps to configure Pachyderm. Pachyderm needs a relational database management system (RDBMS) to store metadata, and the operator takes care of the Pachyderm and related database components. Pachyderm requires Simple Storage Service (S3) storage to store the Pachyderm...

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