Machine Learning (classic)

Overview

New MLOps (DevOps for Machine Learning) capabilities in Azure Machine Learning brings the sophistication of DevOps to data science, with orchestration and management capabilities to enable effective ML Lifecycle management.

Key Features

- Using Azure Resource Manager an AzureML Service Connection type can be created to access your artifacts in an AzureML workspace. <br/>
- By registering a new version of a model into an AzureML service workspace, a trigger can be configured to kick off a release pipeline. <br/>
- Azure DevOps server task for running published ML pipelines

How to set up your service connection

To create a service connection to access your AzureML artifacts, first select your project. Go to project settings, then service connections and click on Azure Resource Manager.

Select the type of service connection to use for your project, all three options enable you to scope the connectiona workspace.

Select AzureMLWorkspace for the scope level, then fill in the following subsequent parameters.

How to use the Azure DevOps AzureML tasks

Run published pipeline server task

Leverage Azure DevOps agentless tasks to run Azure Machine Learning pipelines. Go to your build pipeline and select agentless job.

Next, search and add ML published Pipeline as a task.

Fill in the parameters. AzureML Workspace will be the service connection name, Pipeline Id will be the published ML pipeline id under the Workspace you selected. Fill in experimentation name and expected pipeline parameters to run the pipeline.

Save your changes, and now you can invoke your published pipeline within your build or release pipeline.

Resources:

MLOps project examples Get started with AzureML What is MLOps Get started with ML Pipelines Create reusable workflows with AzureML and Azure DevOps

Have any issues or feedback? Let us know here - https://feedback.azure.com/forums/257792-machine-learning