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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

Installing Redis on Red Hat OpenShift

Redis is a super-fast in-memory database. Redis provides a key-value store with different data structures, such as lists, for the applications to use. In our case, the video generates a lot of frames and our application will infer these frames and keep a count of frames/images with faces. So, we decided to use Redis to keep an atomic counter.

OpenShift will host the Redis server. You will find the complete non-production Redis setup in the chapter7/redis/redis-server.yaml file. Open the file and paste it into the OpenShift GUI while you are in the face-detection project. Hit the Create button and you will have a running Redis cluster on your platform. The following screenshot shows redis-server.yaml in the OpenShift UI.

Figure 7.11 – Installing the Redis server

Figure 7.11 – Installing the Redis server

Validate that the server is running by checking the services section of the OpenShift console within the wines project, identify the Pods, and validate...

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