What is an Azure Machine Learning workspace?

The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. 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.

Pricing and features available depend on whether Basic or Enterprise edition is selected for the workspace. You select the edition when you create the workspace. You can also upgrade from Basic to Enterprise edition.


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

Workspace taxonomy

The diagram shows the following components of a workspace:

  • A workspace can contain Azure Machine Learning compute instances, cloud resources configured with the Python environment necessary to run Azure Machine Learning.

  • User roles enable you to share your workspace with other users, teams, or projects.

  • Compute targets are used to run your experiments.

  • When you create the workspace, associated resources are also created for you.

  • Experiments are training runs you use to build your models.

  • Pipelines are reusable workflows for training and retraining your model.

  • Datasets aid in management of the data you use for model training and pipeline creation.

  • Once you have a model you want to deploy, you create a registered model.

  • Use the registered model and a scoring script to create a deployment endpoint.

Tools for workspace interaction

You can interact with your workspace in the following ways:


Tools marked (preview) below are currently in public preview. The preview version is provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

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 Studio Python SDK / R SDK CLI VS Code
Create a workspace
Manage workspace access
Upgrade to Enterprise edition
Create and manage compute resources
Create a Notebook VM


Moving your Azure Machine Learning workspace to a different subscription, or moving the owning subscription to a new tenant, is not supported. Doing so may cause errors.

Create a workspace

When you create a workspace, you decide whether to create it with Basic or Enterprise edition. The edition determines the features available in the workspace. Among other features, Enterprise edition gives you access to Azure Machine Learning designer and the studio version of building automated machine learning experiments. For more information and pricing information, see Azure Machine Learning pricing.

There are multiple ways to create a workspace:


The workspace name is case-insensitive.

Upgrade to Enterprise edition

You can upgrade your workspace from Basic to Enterprise edition using Azure portal. You cannot downgrade an Enterprise edition workspace to a Basic edition 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 Azure Machine Learning compute instances 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.

Azure storage account

The Azure Storage account created by default with the workspace is a general-purpose v1 account. You can upgrade this to general-purpose v2 after the workspace has been created by following the steps in the Upgrade to a general-purpose v2 storage account article.


Do not enable hierarchical namespace on the storage account after upgrading to general-purpose v2.

If you want to use an existing Azure Storage account, it cannot be a premium account (Premium_LRS and Premium_GRS). It also cannot have a hierarchical namespace (used with Azure Data Lake Storage Gen2). Neither premium storage or hierarchical namespaces are supported with the default storage account of the workspace. You can use premium storage or hierarchical namespace with non-default storage accounts.

Next steps

To get started with Azure Machine Learning, see: