💪Creating an Azure Machine Learning Workspace and Datastores using Bicep

Quickly deploy a Machine Learning solution in Azure using Infrastructure-As-Code with Azure Bicep.

Creating an Azure Machine Learning Workspace and Datastores using Bicep

You leverage Machine Learning in Azure as a cloud service for accelerating and managing the machine learning project lifecycle.

In previous articles, I referred to the core components of the Azure Machine Learning service:

• Workspace: This is the core component. Check how you can create an Azure Machine Learning Workspace.
• Managed resources: These are Azure Machine Learning Compute nodes to use for your development environment. Compute Clusters are used for submitting training runs.
• Linked Services: These include Datastores and Compute targets.
• Assets: This can be an environment, experiments, pipelines, datasets, models, and/or endpoints.
• Dependencies: These are resources needed to execute your AML Workspace properly.

The figure below represents the Azure Machine Learning Architecture:

Azure Machine Learning Architecture