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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

Provisioning an S3 store

ML loves data. A lot of data! S3-compatible object storage software is becoming the de facto standard for storing and retrieving unstructured data at scale and is available on all three big cloud vendors. You can leverage Kubernetes-native open source tools such as MinIO to provision an S3-compatible object store on your OpenShift cluster. MinIO is a high-performance, S3-compatible object store that can be deployed on OpenShift, through which you can use it on-premises and in the cloud.

Red Hat also provides an integrated storage component on the OpenShift platform, named Open Data Foundation, that provides an S3-compatible API. Any standard S3-compatible object storage product will work with ODS. For this book, we’ve chosen MinIO for simplicity. So, let’s start by installing MinIO on the OpenShift platform.

From the code repository for this book, go to the chapter3 folder and find the minio-complete.yaml file. The following steps show how...

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