Sample files used to set up an E2E training and deployment pipeline with the Azure ML CLI. For more info, please visit https://docs.microsoft.com/azure/machine-learning/service/reference-azure-machine-learning-cli

Install the Machine Learning DevOps extension in your project here https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml to scope your project to your Azure Machine Learning service workspace.

This example requires familiarity with Azure Pipelines. For more information, see https://docs.microsoft.com/azure/devops/pipelines/create-first-pipeline?view=azure-devops&tabs=tfs-2018-2.

This example also requires an Azure Machine Learning service workspace. For more information, see https://docs.microsoft.com/azure/machine-learning/service/setup-create-workspace.

You can clone this repo and use it with Azure Pipelines. Before creating the pipeline you must do the following:

1. Create a service connection named azmldemows. This connection must reference your Azure subscription and the Azure resource group that contains your Azure Machine Learning service workspace.
2. Modify the azure-pipelines.yml and change myresourcegroup to the Azure resource group that contains your workspace. You must also change the myworkspace entry to the name of your Azure Machine Learning service workspace.
3. When creating the pipeline for the project, you can point it to the azure-pipelines.yml file. This defines an example pipeline.