This template shows how to perform DevOps for Machine learning applications using Azure Machine Learning powered GitHub Actions. Using this template you will be able to setup your train and deployment infra, train the model and deploy them in an automated manner.

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:

An Azure service principal needs to be generated. Just go to the Azure Portal to find the details of your resource group. 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.

We also need GH PAT token with repo access so that we can trigger a GH workflow when the training is completed on Azure Machine Learning. Add the PAT token with as a secret with the name PATTOKEN in your GitHub repository:

We have precreated a GitHub Workflow file that does the infrastructure creation, trains the model and on successful training completions triggers another workflow that deploys the model.

You need to update this workflow action resource_group parameter that you have used when generating the azure credentials. You can modify the default workspace used in the action. Also, make sure your workspace name and resource group name in workspace.json is same as that in workflow

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.

The above commit would trigger a workflow run and would auto-trigger another GH Workflow YML which deploys the trained model on an Azure Kubernetes Cluster. The log outputs of your action will provide URLs for you to view the resources that have been created in AML. Alternatively, you can visit the Machine Learning Studio to view the progress of your runs, etc. For more details, read the documentation below.

Now you can start modifying the code in the code folder, so that your model and not the provided sample model gets trained on Azure. Where required, modify the environment yaml so that the training and deployment environments will have the correct packages installed in the conda environment for your training and deployment. Upon pushing the changes, actions will kick off your training and deployment run. Check the actions tab to view if your actions have successfully run.

Comment lines 39 to 55 in your "/.github/workflows/train_deploy.yml" file if you only want to train the model. Uncomment line 7 to 8, if you only want to kick off the workflow when pushing changes to the "/code/" file.

The workflow file 'deploy_infra.yml' uses action 'mlopstemplates/aml_configure' to deploy arm template to azure. Arm Template is present in './cloud/.azure/' folder (default name='arm_deploy.json') is used to deploy azure resources to azure . It uses the parameters provided in file 'azure.params.json' to create new resources or update the resources if they are already present.

The template uses the open source Azure certified Actions listed below. Click on the links and read the README files for more details.

• aml-workspace - Connects to or creates a new workspace
• aml-compute - Connects to or creates a new compute target in Azure Machine Learning
• aml-run - Submits a ScriptRun, an Estimator or a Pipeline to Azure Machine Learning
• aml-registermodel - Registers a model to Azure Machine Learning
• aml-deploy - Deploys a model and creates an endpoint for the model

Error message:

Message: ***'error': ***'code': 'MissingSubscriptionRegistration', 'message': "The subscription is not registered to use namespace 'Microsoft.KeyVault'. See https://aka.ms/rps-not-found for how to register subscriptions.", 'details': [***'code': 'MissingSubscriptionRegistration', 'target': 'Microsoft.KeyVault', 'message': "The subscription is not registered to use namespace 'Microsoft.KeyVault'. See https://aka.ms/rps-not-found for how to register subscriptions

Solution:

This error message appears, in case the Azure/aml-workspace action tries to create a new Azure Machine Learning workspace in your resource group and you have never deployed a Key Vault in the subscription before. We recommend to create an Azure Machine Learning workspace manually in the Azure Portal. Follow the steps on this website to create a new workspace with the desired name. After ou have successfully completed the steps, you have to make sure, that your Service Principal has access to the resource group and that the details in your /.cloud/.azure/workspace.json" file are correct and point to the right workspace and resource group.

MLOps empowers data scientists and machine learning engineers to bring together their knowledge and skills to simplify the process of going from model development to release/deployment. ML Ops enables you to track, version, test, certify and reuse assets in every part of the machine learning lifecycle and provides orchestration services to streamline managing this lifecycle. This allows practitioners to automate the end to end machine Learning lifecycle to frequently update models, test new models, and continuously roll out new ML models alongside your other applications and services.

This repository enables Data Scientists to focus on the training and deployment code of their machine learning project (code folder of this repository). Once new code is checked into the code folder of the master branch of this repository the GitHub workflow is triggered and open source Azure Machine Learning actions are used to automatically manage the training through to deployment phases.

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.