> ## 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 AI

> Create your first Azure AI project and deploy an intelligent application in minutes.

# Quickstart: Get Started with Azure AI

This quickstart guides you through creating your first Azure AI project and deploying an intelligent application. You'll set up the necessary resources and run your first AI workload in under 15 minutes.

## Prerequisites

Before you begin, ensure you have:

<Check>
  * An Azure account with an active subscription ([Create a free account](https://azure.microsoft.com/pricing/purchase-options/azure-account))
  * Basic familiarity with the Azure portal or command line tools
  * A code editor (VS Code recommended)
</Check>

## Choose Your Path

Select the Azure AI service that best fits your needs:

<Tabs>
  <Tab title="Microsoft Foundry">
    Best for building AI agents, chatbots, and generative AI applications.
  </Tab>

  <Tab title="Azure Machine Learning">
    Best for training custom ML models and MLOps workflows.
  </Tab>

  <Tab title="Azure AI Search">
    Best for adding intelligent search and RAG capabilities to your apps.
  </Tab>
</Tabs>

## Option 1: Microsoft Foundry Quickstart

<Steps>
  <Step title="Create a Foundry Resource">
    Navigate to the [Microsoft Foundry portal](https://ai.azure.com) and sign in with your Azure account.

    Click **Create workspace** and provide:

    * **Workspace name**: A unique identifier for your project
    * **Subscription**: Your Azure subscription
    * **Resource group**: Create new or use existing
    * **Region**: Choose the region closest to you

    Click **Create** to provision your Foundry resource.
  </Step>

  <Step title="Create Your First Agent">
    Once your workspace is ready:

    1. Select **Agents** from the left navigation
    2. Click **+ New agent**
    3. Choose a model (e.g., GPT-4)
    4. Add custom instructions for your agent's behavior
    5. Configure tools if needed (code interpreter, functions, etc.)

    Click **Create** to deploy your agent.
  </Step>

  <Step title="Test Your Agent">
    In the agent playground:

    1. Type a message to your agent
    2. Observe the response and behavior
    3. Adjust instructions or parameters as needed
    4. Test different scenarios

    Your agent is now ready to integrate into applications.
  </Step>
</Steps>

### Deploy with Python SDK

Install the Microsoft Foundry SDK:

```bash theme={null}
pip install azure-ai-projects
```

Create and interact with an agent:

```python theme={null}
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential

# Initialize client
credential = DefaultAzureCredential()
client = AIProjectClient(
    credential=credential,
    subscription_id="your-subscription-id",
    resource_group_name="your-resource-group",
    project_name="your-project-name"
)

# Create an agent
agent = client.agents.create(
    model="gpt-4",
    name="assistant",
    instructions="You are a helpful AI assistant."
)

# Create a thread and send a message
thread = client.agents.create_thread()
message = client.agents.create_message(
    thread_id=thread.id,
    role="user",
    content="What are the key features of Azure AI?"
)

# Run the agent
run = client.agents.create_run(
    thread_id=thread.id,
    agent_id=agent.id
)

# Get the response
response = client.agents.get_message(
    thread_id=thread.id,
    message_id=run.message_id
)

print(response.content)
```

<Note>
  The Microsoft Foundry API provides a consistent contract for working across different model providers including Azure OpenAI, DeepSeek, xAI, and marketplace models.
</Note>

## Option 2: Azure Machine Learning Quickstart

<Steps>
  <Step title="Create a Workspace">
    Sign in to [Azure Machine Learning studio](https://ml.azure.com).

    Click **Create workspace** and provide:

    * **Workspace name**: Unique name for your workspace
    * **Subscription**: Your Azure subscription
    * **Resource group**: Create or select existing
    * **Region**: Choose your preferred region

    Click **Review + Create**, then **Create**.
  </Step>

  <Step title="Create a Compute Instance">
    A compute instance is your cloud development environment.

    1. Select your workspace
    2. Click **New** → **Compute instance**
    3. Provide a name and keep defaults
    4. Click **Create**

    Wait a few minutes for provisioning to complete.
  </Step>

  <Step title="Run Your First Notebook">
    1. Navigate to **Notebooks** in the left panel
    2. Click **Samples** to view sample notebooks
    3. Browse to **SDK v2** folder
    4. Select a quickstart notebook (e.g., `quickstart.ipynb`)
    5. Click **Clone** to copy to your workspace
    6. Select your compute instance
    7. Run the cells to train your first model
  </Step>
</Steps>

### Train a Model with Python

Install the Azure ML SDK:

```bash theme={null}
pip install azure-ai-ml azure-identity
```

Submit a training job:

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

# Connect to workspace
ml_client = MLClient(
    DefaultAzureCredential(),
    subscription_id="your-subscription-id",
    resource_group_name="your-resource-group",
    workspace_name="your-workspace"
)

# Define training job
job = command(
    code="./src",
    command="python train.py --data ${{inputs.data}}",
    inputs={"data": "azureml:training-data:1"},
    environment="azureml:sklearn-env:1",
    compute="cpu-cluster",
    display_name="quickstart-train"
)

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

## Option 3: Azure AI Search Quickstart

<Steps>
  <Step title="Create a Search Service">
    1. Open the [Azure portal](https://portal.azure.com)
    2. Click **Create a resource** → Search for "Azure AI Search"
    3. Click **Create** and fill in:
       * **Service name**: Unique name for your search service
       * **Subscription**: Your Azure subscription
       * **Resource group**: Create or select existing
       * **Location**: Choose your region
       * **Pricing tier**: Start with Free or Basic
    4. Click **Review + Create**, then **Create**
  </Step>

  <Step title="Create Your First Index">
    In the Azure portal, navigate to your search service:

    1. Click **Import data** to use the wizard
    2. Connect to a data source (Azure Blob Storage, SQL, etc.)
    3. Optionally add AI enrichment for text extraction and analysis
    4. Define index fields and attributes
    5. Create an indexer to populate the index

    Or create an index programmatically using the SDK.
  </Step>

  <Step title="Query Your Index">
    Use the **Search Explorer** in the portal:

    1. Select your index
    2. Enter a search query
    3. View results and refine your query
    4. Test different query types (full-text, vector, hybrid)
  </Step>
</Steps>

### Query with Python SDK

Install the Azure Search SDK:

```bash theme={null}
pip install azure-search-documents azure-identity
```

Perform a search query:

```python theme={null}
from azure.search.documents import SearchClient
from azure.core.credentials import AzureKeyCredential

# Initialize client
search_client = SearchClient(
    endpoint="https://your-service.search.windows.net",
    index_name="your-index",
    credential=AzureKeyCredential("your-api-key")
)

# Perform a search
results = search_client.search(
    search_text="machine learning",
    select=["title", "content", "category"],
    top=10
)

# Display results
for result in results:
    print(f"Title: {result['title']}")
    print(f"Content: {result['content'][:200]}...")
    print(f"Score: {result['@search.score']}")
    print("---")
```

## Next Steps

Now that you've completed the quickstart, explore these resources:

<CardGroup cols={2}>
  <Card title="Build an AI Agent" icon="robot" href="/tutorials/build-agent">
    Create a multi-tool agent with memory and knowledge integration
  </Card>

  <Card title="Train a Custom Model" icon="brain" href="/tutorials/train-model">
    Learn MLOps best practices for production deployments
  </Card>

  <Card title="Implement RAG" icon="book" href="/tutorials/rag-application">
    Build a retrieval-augmented generation app with AI Search
  </Card>

  <Card title="Explore Samples" icon="code" href="/samples">
    Browse code samples for common scenarios
  </Card>
</CardGroup>

## Troubleshooting

<Accordion title="Authentication Issues">
  If you encounter authentication errors:

  * Ensure you're logged in to the correct Azure account
  * Verify your subscription has the necessary permissions
  * Check that your service principal has the required roles
  * Try using `az login` to refresh credentials
</Accordion>

<Accordion title="Resource Creation Failures">
  Common reasons for failures:

  * Insufficient quota in the selected region
  * Resource name already in use (names must be globally unique)
  * Missing permissions on the subscription or resource group
  * Region doesn't support the selected tier or features
</Accordion>

<Accordion title="SDK Installation Problems">
  If pip installation fails:

  * Upgrade pip: `pip install --upgrade pip`
  * Use a virtual environment: `python -m venv venv`
  * Check Python version (3.8+ recommended)
  * Try installing with `--user` flag
</Accordion>

<Tip>
  **Free Trial**: New Azure accounts receive free credits. Use the Free tier for Foundry exploration and AI Search, and leverage free compute hours in Azure Machine Learning.
</Tip>

## Additional Resources

* [Azure AI Services Overview](/services/overview)
* [Microsoft Foundry Documentation](/services/ai-foundry)
* [Azure ML Documentation](/services/machine-learning)
* [Azure AI Search Documentation](/services/ai-search)
* [GitHub Code Samples](https://github.com/Azure-Samples)
* [Microsoft Learn Training Paths](https://learn.microsoft.com/training/azure/)