Here we are trying to deploy the Machine Learning model from GitHub without accessing the Azure for deployment.

The following prerequisites are required to make this repository work:

• Azure subscription
• Contributor access to the Azure subscription
• Access to GitHub Actions

If you don’t have an Azure subscription, create a free account before you begin. Try the free or paid version of Azure Machine Learning today.

To get started with ML Ops, simply create a new repo based off this template, by clicking on the green "Use this template" button:

A service principal needs to be generated for authentication and getting access to your Azure subscription. We suggest adding a service principal with contributor rights to a new resource group or to the one where you have deployed your existing Azure Machine Learning workspace. Just go to the Azure Portal to find the details of your resource group or workspace. Then start the Cloud CLI or install the Azure CLI on your computer and execute the following command to generate the required credentials:

# Replace {service-principal-name}, {subscription-id} and {resource-group} with your 
# Azure subscription id and resource group name and any name for your service principle
az ad sp create-for-rbac --name {service-principal-name} \
                         --role contributor \
                         --scopes /subscriptions/{subscription-id}/resourceGroups/{resource-group} \
                         --sdk-auth

This will generate the following JSON output:

{
  "clientId": "<GUID>",
  "clientSecret": "<GUID>",
  "subscriptionId": "<GUID>",
  "tenantId": "<GUID>",
  (...)
}

Add this JSON output as a secret with the name AZURE_CREDENTIALS in your GitHub repository:

To do so, click on the Settings tab in your repository, then click on Secrets and finally add the new secret with the name AZURE_CREDENTIALS to your repository.

Please follow this link for more details.

You have to modify the parameters in the /.cloud/.azure/workspace.json" file in your repository, so that the GitHub Actions create or connect to the desired Azure Machine Learning workspace. Just click on the link and edit the file.

Please use the same value for the resource_group parameter that you have used when generating the azure credentials. If you already have an Azure ML Workspace under that resource group, change the name parameter in the JSON file to the name of your workspace, if you want the Action to create a new workspace in that resource group, pick a name for your new workspace, and assign it to the name parameter. You can also delete the name parameter, if you want the action to use the default value, which is the repository name.

Once you save your changes to the file, the predefined GitHub workflow that trains and deploys a model on Azure Machine Learning gets triggered. Check the actions tab to view if your actions have successfully run.