> ## 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.

# Quickstart - Get Started with Azure Machine Learning

> Create your first Azure Machine Learning workspace and compute instance to start building ML models.

# Quickstart: Get Started with Azure Machine Learning

In this quickstart, you create the resources needed to start working with Azure Machine Learning and train your first model.

<Info>
  This tutorial uses Azure Machine Learning studio for a streamlined getting-started experience. You can also use the SDK, CLI, or Azure portal.
</Info>

## Prerequisites

* An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/pricing/purchase-options/azure-account)

## What You'll Create

<CardGroup cols={2}>
  <Card title="Workspace" icon="building">
    Central resource to view and manage all ML artifacts
  </Card>

  <Card title="Compute Instance" icon="server">
    Preconfigured cloud resource for development and training
  </Card>
</CardGroup>

## Step 1: Create a Workspace

The workspace is the top-level resource for your machine learning activities, providing a centralized place to view and manage artifacts.

<Steps>
  <Step title="Sign in to Azure ML Studio">
    Navigate to [Azure Machine Learning studio](https://ml.azure.com) and sign in with your Azure account.
  </Step>

  <Step title="Create Workspace">
    Select **Create workspace** and provide the following information:

    | Field          | Description                                          |
    | -------------- | ---------------------------------------------------- |
    | Workspace name | Enter a unique name (case-insensitive)               |
    | Friendly name  | Optional display name with spaces/special characters |
    | Hub            | Leave blank if you don't have access to a hub        |
  </Step>

  <Step title="Configure Advanced Settings">
    If you didn't select a hub, provide:

    * **Subscription**: Your Azure subscription
    * **Resource group**: Use existing or create new
    * **Region**: Select closest to your users/data
  </Step>

  <Step title="Create">
    Select **Create** to provision the workspace and all required resources.
  </Step>
</Steps>

<Note>
  Workspace creation automatically provisions Azure Storage, Container Registry, Key Vault, and Application Insights.
</Note>

## Step 2: Create a Compute Instance

A compute instance is a preconfigured cloud-based compute resource for running Jupyter notebooks and Python scripts.

<Steps>
  <Step title="Open Workspace">
    Select your newly created workspace in the studio.
  </Step>

  <Step title="Create Compute">
    1. On the top right, select **New**
    2. Select **Compute instance** from the list
  </Step>

  <Step title="Configure Instance">
    * **Name**: Enter a unique compute instance name
    * **Virtual machine size**: Keep default or select based on needs
    * **Settings**: Keep defaults unless required by policy
  </Step>

  <Step title="Review and Create">
    Select **Review + Create**, then **Create**
  </Step>
</Steps>

<Warning>
  The compute instance will incur costs while running. Enable idle shutdown to minimize costs.
</Warning>

## Step 3: Explore Azure ML Studio

The studio provides a comprehensive interface for machine learning development:

### Authoring Section

<Tabs>
  <Tab title="Notebooks">
    Create and run Jupyter notebooks with built-in compute integration

    * Clone sample notebooks
    * Create custom notebooks
    * Run Python scripts
  </Tab>

  <Tab title="Automated ML">
    Build models without writing code

    * Upload datasets
    * Configure training settings
    * Automatically find best model
  </Tab>

  <Tab title="Designer">
    Drag-and-drop interface for ML pipelines

    * Prebuilt components
    * Visual workflow design
    * No code required
  </Tab>
</Tabs>

### Assets Section

Track resources created during ML development:

* **Data**: Registered datasets and data assets
* **Models**: Trained and registered models
* **Environments**: Software dependencies
* **Components**: Reusable pipeline steps
* **Jobs**: Training run history

### Manage Section

Create and configure compute and external services:

* Compute instances and clusters
* Datastores and connections
* Endpoints for deployment
* Data labeling projects

## Step 4: Run Your First Notebook

<Steps>
  <Step title="Access Samples">
    1. Navigate to **Notebooks** in the left menu
    2. Select the **Samples** tab at the top
  </Step>

  <Step title="Clone Example">
    1. Open the **SDK v2** folder
    2. Browse to a tutorial notebook
    3. Select **Clone this notebook** to copy to your workspace
  </Step>

  <Step title="Run Notebook">
    1. Open the cloned notebook from **Files**
    2. Select your compute instance at the top
    3. Run cells using **Shift+Enter** or the **Run** button
  </Step>
</Steps>

## Connect to Your Workspace Programmatically

After creating your workspace, connect using the SDK:

<CodeGroup>
  ```python Python SDK theme={null}
  from azure.ai.ml import MLClient
  from azure.identity import DefaultAzureCredential

  # Enter your workspace details
  subscription_id = "<SUBSCRIPTION_ID>"
  resource_group = "<RESOURCE_GROUP>"
  workspace_name = "<WORKSPACE_NAME>"

  # Connect to workspace
  ml_client = MLClient(
      DefaultAzureCredential(),
      subscription_id,
      resource_group,
      workspace_name
  )

  print(f"Connected to workspace: {ml_client.workspace_name}")
  ```

  ```bash Azure CLI theme={null}
  # Login to Azure
  az login

  # Set active subscription
  az account set --subscription <SUBSCRIPTION_ID>

  # List workspaces
  az ml workspace list --resource-group <RESOURCE_GROUP>
  ```
</CodeGroup>

## Sample Training Job

Submit a simple training job using the Python SDK:

```python theme={null}
from azure.ai.ml import command
from azure.ai.ml import Input

# Define training job
job = command(
    code="./src",
    command="python train.py --data ${{inputs.data}}",
    inputs={
        "data": Input(type="uri_folder", path="azureml://datastores/workspaceblobstore/paths/data")
    },
    environment="AzureML-sklearn-1.0@latest",
    compute="cpu-cluster",
    display_name="quickstart-training-job",
)

# Submit job
returned_job = ml_client.jobs.create_or_update(job)
print(f"Job submitted: {returned_job.name}")
```

## Cost Management

<Accordion title="Minimize Azure ML Costs">
  To avoid charges when resources are idle:

  * **Compute clusters**: Set minimum nodes to 0
  * **Compute instances**: Enable idle shutdown
  * **Serverless compute**: No configuration needed - automatically scales to zero

  <Note>
    You still pay for disk, public IP, and load balancer when compute instance is stopped.
  </Note>
</Accordion>

## Next Steps

<CardGroup cols={2}>
  <Card title="Train Your First Model" icon="graduation-cap" href="/machine-learning/training/overview">
    Learn how to train machine learning models
  </Card>

  <Card title="Workspace Concepts" icon="book" href="/machine-learning/concepts/workspace">
    Deep dive into workspace features
  </Card>

  <Card title="Compute Resources" icon="server" href="/machine-learning/concepts/compute">
    Understand compute options
  </Card>

  <Card title="Deploy Models" icon="rocket" href="/machine-learning/deployment/overview">
    Deploy models to production
  </Card>
</CardGroup>

## Additional Resources

* [Azure Machine Learning Samples](https://github.com/Azure/azureml-examples)
* [Python SDK Documentation](https://learn.microsoft.com/python/api/overview/azure/ml/)
* [CLI v2 Reference](https://learn.microsoft.com/cli/azure/ml)