> ## 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 Overview

> Learn about Azure Machine Learning resources, assets, and how to build, train, and deploy machine learning models at scale.

# Azure Machine Learning Overview

Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. Machine learning professionals, data scientists, and engineers can use it to train and deploy models at scale, and to manage MLOps workflows.

<Info>
  Azure Machine Learning provides a unified platform for the complete machine learning lifecycle, from data preparation through model deployment and monitoring.
</Info>

## Key Concepts

Azure Machine Learning includes several resources and assets to enable you to perform your machine learning tasks:

### Resources

Setup or infrastructural resources needed to run a machine learning workflow:

<CardGroup cols={3}>
  <Card title="Workspace" icon="building" href="/machine-learning/concepts/workspace">
    Top-level resource providing centralized place to work with all artifacts
  </Card>

  <Card title="Compute" icon="server" href="/machine-learning/concepts/compute">
    Designated compute resources for training and inference
  </Card>

  <Card title="Datastore" icon="database">
    Securely store connection information for data storage
  </Card>
</CardGroup>

### Assets

Versioned assets created and registered in your workspace:

<CardGroup cols={2}>
  <Card title="Models" icon="brain">
    Trained machine learning models tracked and versioned
  </Card>

  <Card title="Environments" icon="box">
    Encapsulation of software packages and settings
  </Card>

  <Card title="Data" icon="chart-line">
    URIs and tables for training and inference
  </Card>

  <Card title="Components" icon="puzzle-piece" href="/machine-learning/components/overview">
    Reusable pipeline steps for ML workflows
  </Card>
</CardGroup>

## Azure Machine Learning SDK

The Python SDK v2 provides a programmatic interface to Azure Machine Learning:

```python theme={null}
from azure.ai.ml import MLClient
from azure.ai.ml.entities import Workspace
from azure.identity import DefaultAzureCredential

# Connect to your subscription
subscription_id = "<SUBSCRIPTION_ID>"
resource_group = "<RESOURCE_GROUP>"

ml_client = MLClient(
    DefaultAzureCredential(), 
    subscription_id, 
    resource_group
)
```

## Azure CLI Extension

The Azure CLI `ml` extension (v2) enables machine learning operations from the command line:

```bash theme={null}
az login
az account set --subscription <SUBSCRIPTION_ID>

# Create a workspace
az ml workspace create --file workspace.yml
```

## Machine Learning Workflow

<Steps>
  <Step title="Prepare Data">
    Connect to datastores and create data assets for training
  </Step>

  <Step title="Train Models">
    Submit training jobs to compute targets with your training script
  </Step>

  <Step title="Register Models">
    Store and version trained models in the model registry
  </Step>

  <Step title="Deploy Models">
    Create endpoints for real-time or batch inference
  </Step>

  <Step title="Monitor">
    Track model performance and data drift in production
  </Step>
</Steps>

## Development Environments

Azure Machine Learning supports multiple development tools:

<Tabs>
  <Tab title="Studio">
    Web-based interface for no-code and code-first experiences

    * Notebooks for interactive development
    * Automated ML for no-code model training
    * Designer for drag-and-drop workflows
  </Tab>

  <Tab title="SDK">
    Python SDK v2 for programmatic access

    ```python theme={null}
    from azure.ai.ml import MLClient
    from azure.ai.ml.entities import AmlCompute
    ```
  </Tab>

  <Tab title="CLI">
    Command-line interface for automation

    ```bash theme={null}
    az ml job create --file job.yml
    ```
  </Tab>

  <Tab title="VS Code">
    Integrated development with the Azure Machine Learning extension
  </Tab>
</Tabs>

## Storage Format Support

Azure Machine Learning supports three types of storage formats for models:

| Format         | Description                          |
| -------------- | ------------------------------------ |
| `custom_model` | Standard model format                |
| `mlflow_model` | MLflow packaged models with metadata |
| `triton_model` | NVIDIA Triton inference models       |

## Next Steps

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/machine-learning/quickstart">
    Get started with Azure Machine Learning
  </Card>

  <Card title="Training" icon="graduation-cap" href="/machine-learning/training/overview">
    Learn how to train models
  </Card>

  <Card title="Deployment" icon="cloud-arrow-up" href="/machine-learning/deployment/overview">
    Deploy models to production
  </Card>

  <Card title="MLOps" icon="gears" href="/machine-learning/concepts/mlops">
    Manage the model lifecycle
  </Card>
</CardGroup>