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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

Bringing it all together

Start with updating the frontend code with the HTTP address for face-detection-app as in the refreshFaceCounter and takepicture functions. Keep in mind that your URL will be different.

Save and load the HTML file into your browser. The browser will throw a warning that the application is trying to capture video feed; allow the application to access the video feed. You will get a screen like the one shown in Figure 7.23.

Figure 7.23 – Application UI capturing video and inferencing

Figure 7.23 – Application UI capturing video and inferencing

The web page captures the video stream from your laptop camera and displays it in the top area of the page. The middle area shows the image capture every 250 milliseconds as configured on the web page, and the bottom counter displays the number of images captured.

You will notice that the counter is continuously incremented while the person sits in front of the camera. This means that every 250 milliseconds, an image has been captured and...

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