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:  02 Oct 2026
Skill level:
  Introductory
Reading time:
  45 min
Last updated:
  02 Oct 2026
Author:Pareena Verma, Arm
Arm IP:Neoverse
Tags: ML Google Axion Linux OpenCV Python NumPy Flask
Author:
  • Pareena Verma, Arm
Arm IP:
Neoverse
Tags:
ML Google Axion 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 virtual machines (VMs).
  • 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 and video processing concepts

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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You’ll build browser-based computer-vision pipelines with OpenCV on a Google Cloud C4A Axion VM. First, you’ll provision a SUSE Linux VM, open port 8000, and prepare a Python 3.11 virtual environment. Then, you’ll create image and video pipelines that process frames and serve updated output through an HTTP server. Finally, you’ll add a machine learning model, stream its visual predictions, and validate the output from the VM’s external IP address.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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Which C4A machine type should I use?
Use the c4a-standard-4 machine type with four vCPUs and 16 GB of memory.
Which port do I open for the browser view, and how do I check that it works?
Open TCP port 8000 with a VPC firewall rule. After you start the project’s HTTP server, visit http://EXTERNAL_IP:8000. A page that loads confirms the rule and service are configured.
What Python version do I need during setup?
Install Python 3.11. Additionally, install development tools with zypper to support building OpenCV’s pip package.
Where are the project files and virtual environment created?
Use the ~/opencv-project directory and a virtual environment named cv-env. Run subsequent scripts from that directory with the environment activated.
How do I save the trained model and load it in the OpenCV pipeline?
Use joblib.dump in the training script to save the classifier as iris_model.joblib and the label names as iris_labels.joblib. In the OpenCV pipeline, use joblib.load to load both files so you can generate predictions without retraining the model.
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