What is an Azure Machine Learning service workspace?

The workspace is the top-level resource for Azure Machine Learning service, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning service. The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts. You use this information to determine which training run produces the best model.

Once you have a model you like, you register it with the workspace. You then use the registered model and scoring scripts to deploy to Azure Container Instances, Azure Kubernetes Service, or to a field-programmable gate array (FPGA) as a REST-based HTTP endpoint. You can also deploy the model to an Azure IoT Edge device as a module.


A taxonomy of the workspace is illustrated in the following diagram:

Workspace taxonomy

The diagram shows the following components of a workspace:

Tools for workspace interaction

You can interact with your workspace in the following ways:

Machine learning with a workspace

Machine learning tasks read and/or write artifacts to your workspace.

  • Run an experiment to train a model - writes experiment run results to the workspace.
  • Use automated ML to train a model - writes training results to the workspace.
  • Register a model in the workspace.
  • Deploy a model - uses the registered model to create a deployment.
  • Create and run reusable workflows.
  • View machine learning artifacts such as experiments, pipelines, models, deployments.
  • Track and monitor models.

Workspace management

You can also perform the following workspace management tasks:

Workspace management task Portal SDK CLI
Create a workspace
Create and manage compute resources
Manage workspace access
Create a notebook VM

Create a workspace

There are multiple ways to create a workspace.

Associated resources

When you create a new workspace, it automatically creates several Azure resources that are used by the workspace:

  • Azure Container Registry: Registers docker containers that you use during training and when you deploy a model. To minimize costs, ACR is lazy-loaded until deployment images are created.
  • Azure Storage account: Is used as the default datastore for the workspace. Jupyter notebooks that are used with your notebook VMs are stored here as well.
  • Azure Application Insights: Stores monitoring information about your models.
  • Azure Key Vault: Stores secrets that are used by compute targets and other sensitive information that's needed by the workspace.


In addition to creating new versions, you can also use existing Azure services.

Next steps

To get started with Azure Machine Learning service, see: