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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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

A/B testing 156

Advanced Cluster Security (ACS) 6

Amazon Web Services (AWS) 17, 204

Application Programming Interfaces (APIs) 187

Azure Red Hat OpenShift (ARO) 6

B

base image 8

basic data science pipeline

building 114-119

building blocks, RHODS

data ingestion 15

data management 15

data storage 15

GPU 15

model development and tuning 14

model serving and observability 16

C

canary deployment 154

canary model deployment 154-156

canary release 154

Center for Internet Security (CIS) 10

cloud

advantages 17

cloud infrastructure costs

optimizing 172-175

cluster 7

cluster autoscaling 174

Common Vulnerabilities and Exposures (CVE) 157

container 7

container image 7

Container Runtime Interface (CRI) 7

cost, for...

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