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 an AzureML workspace.
- By registering a new version of a model into an AzureML service workspace, a trigger can be configured to kick off a release pipeline.
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 AzureMLWorkspace for the scope level, then fill in the following subsequent parameters.
How to enable model triggering in a release pipeline:
Go to your release pipeline and add a new artifact.
Click on AzureML Model artifact then select the appropriate AzureML service connection and model.
Enable the deployment trigger on your model artifact as shown here.
Every time a new version of that model is registered, a release pipeline will be triggered.
Resources:
For sample projects and examples check out aka.ms/mlops
Get started with AzureML - https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml
MLOps with AzureML - https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-model-management-and-deployment
Have any issues or feedback?
Let us know here - https://feedback.azure.com/forums/257792-machine-learning
