> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/MicrosoftDocs/azure-ai-docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Azure Machine Learning Workspace

> Learn about Azure Machine Learning workspaces, how to organize them, and manage access for your team.

# What is an Azure Machine Learning Workspace?

Workspaces are places to collaborate with colleagues to create machine learning artifacts and group related work. They provide a centralized environment for experiments, jobs, datasets, models, components, and inference endpoints.

<Info>
  The workspace is the top-level resource for Azure Machine Learning, keeping a history of all jobs, logs, metrics, output, and script snapshots.
</Info>

## Tasks Performed Within a Workspace

For machine learning teams, the workspace organizes the following activities:

<CardGroup cols={2}>
  <Card title="Create Jobs" icon="play">
    Training runs to build models, grouped into experiments for metric comparison
  </Card>

  <Card title="Author Pipelines" icon="diagram-project">
    Reusable workflows for training and retraining models
  </Card>

  <Card title="Register Data Assets" icon="database">
    Manage data used for model training and pipeline creation
  </Card>

  <Card title="Register Models" icon="cube">
    Version and track models ready for deployment
  </Card>

  <Card title="Create Endpoints" icon="plug">
    Deploy registered models for real-time or batch inference
  </Card>

  <Card title="Manage Compute" icon="server">
    Configure compute targets for running experiments
  </Card>
</CardGroup>

## Workspace Components

### Resource Configurations

Workspaces host the following resource configurations:

<Tabs>
  <Tab title="Compute Targets">
    Run experiments and training jobs:

    * **Compute instances**: Development workstations
    * **Compute clusters**: Scalable training infrastructure
    * **Serverless compute**: On-demand compute without management
    * **Inference clusters**: AKS for model deployment
  </Tab>

  <Tab title="Datastores">
    Define connections to data sources:

    * Azure Blob Storage
    * Azure File Share
    * Azure Data Lake Storage Gen2
    * Azure SQL Database
  </Tab>

  <Tab title="Security Settings">
    Configure workspace security:

    * Network isolation with VNets
    * Identity and access control (RBAC)
    * Customer-managed encryption keys
    * Private endpoints
  </Tab>
</Tabs>

## Organizing Workspaces

For team leads and administrators, workspaces serve as containers for access management, cost management, and data isolation.

### Best Practices

<AccordionGroup>
  <Accordion title="Use Role-Based Access Control">
    Manage permissions between users with predefined roles:

    * **Owner**: Full workspace access including role assignment
    * **Contributor**: Create and manage resources except role assignment
    * **Reader**: View workspace resources only
    * **Custom roles**: Define specific permissions for your needs

    ```bash theme={null}
    az role assignment create \
      --assignee user@example.com \
      --role "AzureML Data Scientist" \
      --scope /subscriptions/<subscription-id>/resourceGroups/<rg>/providers/Microsoft.MachineLearningServices/workspaces/<workspace>
    ```
  </Accordion>

  <Accordion title="Assign Access to User Groups">
    Use Microsoft Entra user groups instead of individual users:

    * Simplifies permission management
    * Consistent access across resources
    * Easier onboarding/offboarding
  </Accordion>

  <Accordion title="One Workspace Per Project">
    Limit to one project per workspace for:

    * Project-level cost reporting
    * Scoped datastore configuration
    * Better resource organization
    * Clear ownership boundaries
  </Accordion>

  <Accordion title="Share Azure Resources">
    Share associated resources between workspaces:

    * Storage accounts
    * Key Vaults
    * Application Insights
    * Container Registries

    Reduces repetitive setup and infrastructure costs.
  </Accordion>

  <Accordion title="Enable Self-Service">
    IT admins can:

    1. Precreate and secure associated resources
    2. Grant appropriate RBAC roles to data scientists
    3. Allow teams to create workspaces independently

    Balances governance with team autonomy.
  </Accordion>

  <Accordion title="Use Hub Workspaces">
    Group multiple project workspaces with shared:

    * Security settings and connections
    * Compute resources
    * Centralized governance

    Hub workspaces work with both Azure ML studio and Microsoft Foundry.
  </Accordion>
</AccordionGroup>

## Associated Azure Resources

When you create a workspace, Azure Machine Learning automatically provisions these resources:

| Resource                       | Purpose                                                                               |
| ------------------------------ | ------------------------------------------------------------------------------------- |
| **Azure Storage Account**      | Stores job logs, notebooks, and uploaded data. Default datastore for workspace.       |
| **Azure Container Registry**   | Stores Docker images for custom environments. Created on-demand when building images. |
| **Azure Application Insights** | Monitors and collects diagnostic information from inference endpoints.                |
| **Azure Key Vault**            | Stores secrets, connection strings, and keys used by compute and datastores.          |

<Warning>
  **Storage Account Limitations**

  You cannot use:

  * BlobStorage accounts
  * Premium storage accounts (Premium\_LRS/Premium\_GRS)
  * Accounts with hierarchical namespace enabled

  You can attach premium or hierarchical namespace storage as additional datastores.
</Warning>

## How Content is Stored

The workspace keeps a history of all training runs with:

* **Logs**: Console output and error messages
* **Metrics**: Tracked numeric values and visualizations
* **Output**: Model files and artifacts
* **Lineage metadata**: Dataset versions and relationships
* **Script snapshots**: Code used for each run

Artifacts and metadata are stored in the workspace and associated Azure resources.

## Create a Workspace

Multiple methods are available for workspace creation:

<Tabs>
  <Tab title="Azure ML Studio">
    Quick creation with default settings:

    1. Navigate to [ml.azure.com](https://ml.azure.com)
    2. Select **Create workspace**
    3. Provide name, subscription, and resource group
    4. Select **Create**
  </Tab>

  <Tab title="Python SDK">
    ```python theme={null}
    from azure.ai.ml import MLClient
    from azure.ai.ml.entities import Workspace
    from azure.identity import DefaultAzureCredential

    ws = Workspace(
        name="my-workspace",
        location="eastus",
        display_name="My ML Workspace",
        description="Production workspace for ML team",
        tags={"environment": "production", "team": "ml-ops"}
    )

    ml_client = MLClient(
        DefaultAzureCredential(),
        subscription_id="<subscription-id>",
        resource_group="<resource-group>"
    )

    ml_client.workspaces.begin_create(ws).result()
    ```
  </Tab>

  <Tab title="Azure CLI">
    ```bash theme={null}
    az ml workspace create \
      --name my-workspace \
      --resource-group my-rg \
      --location eastus \
      --display-name "My ML Workspace" \
      --tags environment=production team=ml-ops
    ```
  </Tab>

  <Tab title="ARM Template">
    ```json theme={null}
    {
      "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
      "resources": [
        {
          "type": "Microsoft.MachineLearningServices/workspaces",
          "apiVersion": "2023-04-01",
          "name": "my-workspace",
          "location": "eastus",
          "properties": {
            "friendlyName": "My ML Workspace",
            "description": "Production workspace"
          }
        }
      ]
    }
    ```
  </Tab>
</Tabs>

## Workspace Subresources

Compute clusters and instances create additional subresources:

* **Virtual Machines**: Provide computing power
* **Load Balancer**: Manages traffic (even when stopped)
* **Virtual Network**: Enable resource communication
* **Bandwidth**: Charges for outbound data transfer

<Note>
  These subresources are managed automatically by Azure Machine Learning.
</Note>

## Management Tools

Interact with your workspace using:

<CardGroup cols={2}>
  <Card title="Azure Portal" icon="window">
    Full Azure resource management
  </Card>

  <Card title="ML Studio" icon="desktop">
    ML-specific interface at ml.azure.com
  </Card>

  <Card title="Python SDK" icon="python">
    Programmatic workspace access
  </Card>

  <Card title="Azure CLI" icon="terminal">
    Command-line automation
  </Card>

  <Card title="VS Code Extension" icon="code">
    Integrated development experience
  </Card>

  <Card title="REST API" icon="globe">
    Direct API integration
  </Card>
</CardGroup>

## Cost Management

<Accordion title="Understanding Workspace Costs">
  Workspace costs come from:

  1. **Compute resources**: VMs for training and inference
  2. **Storage**: Blob storage for data and models
  3. **Networking**: Private Link, VNet integration
  4. **Monitoring**: Application Insights and Log Analytics

  To monitor costs:

  ```bash theme={null}
  az consumption usage list \
    --start-date 2024-01-01 \
    --end-date 2024-01-31 \
    | jq '[.[] | select(.instanceName | contains("ml-workspace"))]'
  ```
</Accordion>

## Next Steps

<CardGroup cols={2}>
  <Card title="Create Your Workspace" icon="plus" href="/machine-learning/quickstart">
    Follow the quickstart guide
  </Card>

  <Card title="Compute Targets" icon="server" href="/machine-learning/concepts/compute">
    Learn about compute resources
  </Card>

  <Card title="Security" icon="shield">
    Configure workspace security settings
  </Card>

  <Card title="Hub Workspaces" icon="sitemap">
    Explore enterprise workspace organization
  </Card>
</CardGroup>