Create a project in Azure AI Foundry portal

This article describes how to create an Azure AI Foundry project. A project is used to organize your work and save state while building customized AI apps.

Projects are hosted by an Azure AI Foundry hub. If your company has an administrative team that has created a hub for you, you can create a project from that hub. If you are working on your own, you can create a project and a default hub will automatically be created for you.

For more information about the projects and hubs model, see Azure AI Foundry hubs.

Prerequisites

• An Azure subscription. If you don't have an Azure subscription, create a free account.
• For Python SDK or CLI steps, an Azure AI Foundry hub. If you don't have a hub, see How to create and manage an Azure AI Foundry hub.
• For Azure AI Foundry, a hub isn't required. It is created for you when needed.

Create a project

Use the following tabs to select the method you plan to use to create a project:

Azure AI Foundry

To create a project in Azure AI Foundry, follow these steps:

1. Go to Azure AI Foundry. If you are in a project, select Azure AI Foundry at the top left of the page to go to the Home page.

2. Select + Create project.

3. Enter a name for the project.

4. If you have a hub, you'll see the one you most recently used selected.

   • If you have access to more than one hub, you can select a different hub from the dropdown.

   • If you want to create a new one, select Create new hub and supply a name. If you want to customize the default values, see the next section.

     

5. If you don't have a hub, a default one is created for you. If you want to customize the default values, see the next section.

6. Select Create a project. Or, if you want to customize a new hub, follow the steps in the next section.

Customize the hub

Projects live inside a hub. A hub allows you to share configurations like data connections with all projects, and to centrally manage security settings and spend. If you're part of a team, hubs are shared across other team members in your subscription. For more information about the relationship between hubs and projects, see the hubs and projects overview documentation.

When you create a new hub, you must have Owner or Contributor permissions on the selected resource group. If you're part of a team and don't have these permissions, your administrator should create a hub for you.

While you can create a hub as part of the project creation, you have more control and can set more advanced settings for the hub if you create it separately. For example, you can customize network security or the underlying Azure Storage account. For more information, see How to create and manage an Azure AI Foundry hub.

When you create a new hub as part of the project creation, default settings are provided. If you want to customize these settings, do so before you create the project:

1. In the Create a project form, select Customize.

   

2. Select an existing Resource group you want to use, or leave the default to create a new resource group.

   Tip

   Especially for getting started it's recommended to create a new resource group for your project. This allows you to easily manage the project and all of its resources together. When you create a project, several resources are created in the resource group, including a hub, a container registry, and a storage account.

3. Select a Location or use the default. The location is the region where the hub is hosted. The location of the hub is also the location of the project. Azure AI services availability differs per region. For example, certain models might not be available in certain regions.

4. Select an existing Azure AI services resource (including Azure OpenAI) from the dropdown if you have one, or use the default to create a new resource.

   

5. Select Create a project. You see progress of resource creation and the project is created when the process is complete.

   

Python SDK

The code in this section assumes you have an existing hub. If you don't have a hub, see How to create and manage an Azure AI Foundry hub to create one.

1. Install Python as described in the SDK quickstart.

2. Install the Azure Machine Learning SDK v2.

3. Install azure-identity: pip install azure-identity. If in a notebook cell, use %pip install azure-identity.

4. Provide your subscription details:

   # Enter details of your subscription
   subscription_id = "<SUBSCRIPTION_ID>"
   resource_group = "<RESOURCE_GROUP>"

5. Get a handle to the subscription. All the Python code in this article uses ml_client:

   # get a handle to the subscription
   
   from azure.ai.ml import MLClient
   from azure.identity import DefaultAzureCredential
   
   ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group)

6. (Optional) If you have multiple accounts, add the tenant ID of the Microsoft Entra ID you wish to use into the DefaultAzureCredential. Find your tenant ID from the Azure portal under Microsoft Entra ID, External Identities.

   DefaultAzureCredential(interactive_browser_tenant_id="<TENANT_ID>")
   

7. (Optional) If you're working on in the Azure Government - US or Azure China 21Vianet regions, specify the region into which you want to authenticate. You can specify the region with DefaultAzureCredential. The following example authenticates to the Azure Government - US region:

   from azure.identity import AzureAuthorityHosts
   DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)
   

8. Use the following code to create a project from a hub you or your administrator created previously. Replace example string values with your own values:

   from azure.ai.ml.entities import Project
   
   my_project_name = "myexampleproject"
   my_display_name = "My Example Project"
   hub_name = "myhubname" # Azure resource manager ID of the hub
   hub_id=f"/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.MachineLearningServices/workspaces/{hub_name}"
   
   my_project = Project(name=my_project_name, 
                   display_name=my_display_name,
                   hub_id=hub_id)
   
   created_project = ml_client.workspaces.begin_create(workspace=my_project).result()
   

Azure CLI

The code in this section assumes you have an existing hub. If you don't have a hub, see How to create and manage an Azure AI Foundry hub to create one.

1. If you don't have the Azure CLI and machine learning extension installed, follow the steps in the Install and set up the machine learning extension article.

2. To authenticate to your Azure subscription from the Azure CLI, use the following command:

   az login
   

   For more information on authenticating, see Authentication methods.

3. Once the extension is installed and authenticated to your Azure subscription, use the following command to create a new Azure AI Foundry project from an existing Azure AI Foundry hub:

   az ml workspace create --kind project --hub-id {my_hub_ARM_ID} --resource-group {my_resource_group} --name {my_project_name}
   

View project settings

Azure AI Foundry

On the project Overview page you can find information about the project.

• Name: The name of the project appears in the top left corner. You can rename the project using the edit tool.
• Subscription: The subscription that hosts the hub that hosts the project.
• Resource group: The resource group that hosts the hub that hosts the project.

Select Management center to navigate to the project resources in Azure AI Foundry portal. Select Manage in Azure portal to navigate to the project resources in the Azure portal.

Python SDK

To manage or use the new project, include it in the MLClient:

ml_client = MLClient(workspace_name=my_project_name, resource_group_name=resource_group, subscription_id=subscription_id,credential=DefaultAzureCredential())

Azure CLI

To view settings for the project, use the az ml workspace show command. For example:

az ml workspace show --name {my_project_name} --resource-group {my_resource_group}

Access project resources

Common configurations on the hub are shared with your project, including connections, compute instances, and network access, so you can start developing right away.

In addition, a number of resources are only accessible by users in your project workspace:

• Components including datasets, flows, indexes, deployed model API endpoints (open and serverless).

• Connections created by you under 'project settings.'

• Azure Storage blob containers, and a fileshare for data upload within your project. Access storage using the following connections:

  Data connection	Storage location	Purpose
  workspaceblobstore	{project-GUID}-azureml-blobstore	Default container for data uploads
  workspaceartifactstore	{project-GUID}-azureml	Stores components and metadata for your project such as model weights
  workspacefilestore	{project-GUID}-code	Hosts files created on your compute and using prompt flow

Note

Storage connections are not created directly with the project when your storage account has public network access set to disabled. These are created instead when a first user accesses AI Foundry over a private network connection. Troubleshoot storage connections

Related content

• Deploy an enterprise chat web app
• Learn more about Azure AI Foundry
• Learn more about hubs