Create a project in Azure AI Studio

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

Projects are hosted by an Azure AI Studio hub that provides enterprise-grade security and a collaborative environment. For more information about the projects and resources model, see Azure AI Studio hubs.

Prerequisites

• An Azure subscription. If you don't have an Azure subscription, create a free account.
• An Azure AI Studio hub. If you don't have a hub, see How to create and manage an Azure AI Studio hub.

Create a project

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

Azure AI Studio

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

1. Go to the Home page of Azure AI Studio.

2. Select + New project.

3. Enter a name for the project.

4. Select a hub from the dropdown to host your project. For information about the relationship between hubs and projects, see the hubs and projects overview documentation. If you don't yet have a hub, select Create a new hub.

   

   Note

   To create a hub, you must have Owner or Contributor permissions on the selected resource group. It's recommended to share a hub with your team. This lets you share configurations like data connections with all projects, and centrally manage security settings and spend. For more options to create a hub, see how to create and manage an Azure AI Studio hub. A project name must be unique between projects that share the same hub.

5. If you're creating a new hub, enter a name.

6. Select your Azure subscription from the Subscription dropdown. Choose a specific Azure subscription for your project for billing, access, or administrative reasons. For example, this grants users and service principals with subscription-level access to your project.

7. Leave the Resource group as the default to create a new resource group. Alternatively, you can select an existing resource group from the dropdown.

   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.

8. Enter the Location for the hub and then select Next. 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.

9. Select an existing Azure AI services resource (including Azure OpenAI) from the dropdown or create a new one.

   

10. On the Review and finish page, you see the Azure AI services resource name and other settings to review.

    

11. Review the project details and then select Create a project. You see progress of resource creation and the project is created when the process is complete.

    

Once a project is created, you can access the playground, tools, and other assets in the left navigation panel.

Python SDK

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

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 Studio project from an existing Azure AI Studio 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 Studio

On the project Settings page you can find information about the project, such as the project name, description, and the hub that hosts the project. You can also find the project ID, which is used to identify the project via SDK or API.

• Name: The name of the project corresponds to the selected project in the left panel.
• Hub: The hub that hosts the project.
• Location: The location of the hub that hosts the project. For supported locations, see Azure AI Studio regions.
• 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 Manage in the 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:

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

2. Connections created by you under 'project settings.'

3. 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 Studio over a private network connection. Troubleshoot storage connections

Related content

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