Build computer vision pipelines with OpenCV on a Google Cloud C4A Axion VM
Introduction
Understand OpenCV on Google Axion C4A
Create a firewall rule for OpenCV browser visualization
Create a Google Axion C4A Arm virtual machine
Build OpenCV pipelines on Google Axion
Use machine learning models with OpenCV on Google Axion
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  45 min
Last updated:  31 Jul 2026
Skill level:
  Introductory
Reading time:
  45 min
Last updated:
  31 Jul 2026
Author:Pareena Verma, Arm
Arm IP:Neoverse
Tags: ML Google Cloud Linux OpenCV Python NumPy Flask
Author:
  • Pareena Verma, Arm
Arm IP:
Neoverse
Tags:
ML Google Cloud Linux OpenCV Python NumPy Flask

Who is this for?

This is an introductory topic for DevOps engineers, software developers, and AI practitioners who want to build and run computer vision pipelines on SUSE Linux Enterprise Server (SLES) Arm64 using OpenCV, process images and videos, visualize outputs in real time, and integrate ML models.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Install and configure OpenCV on Google Cloud C4A Axion Arm64 instances
  • Build image processing pipelines using OpenCV
  • Develop video processing pipelines with real-time frame updates
  • Visualize OpenCV outputs in the browser using an HTTP server
  • Integrate OpenCV pipelines with machine learning models

Prerequisites

Before starting, you will need the following:

  • A Google Cloud Platform (GCP) account with billing enabled
  • Basic familiarity with Python and Linux command line
  • Understanding of basic image/video processing concepts
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