How to configure a private link for Azure AI Studio hubs
Important
Some of the features described in this article might only be available in preview. This preview is provided without a service-level agreement, and we don't recommend it for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.
We have two network isolation aspects. One is the network isolation to access an Azure AI Studio hub. Another is the network isolation of computing resources in your hub and projects such as compute instances, serverless, and managed online endpoints. This article explains the former highlighted in the diagram. You can use private link to establish the private connection to your hub and its default resources. This article is for Azure AI Studio (hub and projects). For information on Azure AI services, see the Azure AI services documentation.
You get several hub default resources in your resource group. You need to configure following network isolation configurations.
- Disable public network access of hub default resources such as Azure Storage, Azure Key Vault, and Azure Container Registry.
- Establish private endpoint connection to hub default resources. You need to have both a blob and file private endpoint for the default storage account.
- Managed identity configurations to allow hubs access your storage account if it's private.
- Azure AI Search should be public.
Prerequisites
You must have an existing Azure Virtual Network to create the private endpoint in.
Important
We do not recommend using the 172.17.0.0/16 IP address range for your VNet. This is the default subnet range used by the Docker bridge network or on-premises.
Disable network policies for private endpoints before adding the private endpoint.
Create a hub that uses a private endpoint
Use one of the following methods to create a hub with a private endpoint. Each of these methods requires an existing virtual network:
- From the Azure portal, go to Azure AI Studio and choose + New Azure AI.
- Choose network isolation mode in Networking tab.
- Scroll down to Workspace Inbound access and choose + Add.
- Input required fields. When selecting the Region, select the same region as your virtual network.
Add a private endpoint to a hub
Use one of the following methods to add a private endpoint to an existing hub:
- From the Azure portal, select your hub.
- From the left side of the page, select Networking and then select the Private endpoint connections tab.
- When selecting the Region, select the same region as your virtual network.
- When selecting Resource type, use
azuremlworkspace
. - Set the Resource to your workspace name.
Finally, select Create to create the private endpoint.
Remove a private endpoint
You can remove one or all private endpoints for a hub. Removing a private endpoint removes the hub from the Azure Virtual Network that the endpoint was associated with. Removing the private endpoint might prevent the hub from accessing resources in that virtual network, or resources in the virtual network from accessing the workspace. For example, if the virtual network doesn't allow access to or from the public internet.
Warning
Removing the private endpoints for a hub doesn't make it publicly accessible. To make the hub publicly accessible, use the steps in the Enable public access section.
To remove a private endpoint, use the following information:
- From the Azure portal, select your hub.
- From the left side of the page, select Networking and then select the Private endpoint connections tab.
- Select the endpoint to remove and then select Remove.
Enable public access
In some situations, you might want to allow someone to connect to your secured hub over a public endpoint, instead of through the virtual network. Or you might want to remove the workspace from the virtual network and re-enable public access.
Important
Enabling public access doesn't remove any private endpoints that exist. All communications between components behind the virtual network that the private endpoint(s) connect to are still secured. It enables public access only to the hub, in addition to the private access through any private endpoints.
To enable public access, use the following steps:
- From the Azure portal, select your hub.
- From the left side of the page, select Networking and then select the Public access tab.
- Select Enabled from all networks, and then select Save.
Managed identity configuration
A manged identity configuration is required if you make your storage account private. Our services need to read/write data in your private storage account using Allow Azure services on the trusted services list to access this storage account with following managed identity configurations. Enable the system assigned managed identity of Azure AI Service and Azure AI Search, then configure role-based access control for each managed identity.
Role | Managed Identity | Resource | Purpose | Reference |
---|---|---|---|---|
Storage File Data Privileged Contributor |
Azure AI Studio project | Storage Account | Read/Write prompt flow data. | Prompt flow doc |
Storage Blob Data Contributor |
Azure AI Service | Storage Account | Read from input container, write to pre-process result to output container. | Azure OpenAI Doc |
Storage Blob Data Contributor |
Azure AI Search | Storage Account | Read blob and write knowledge store | Search doc. |
Custom DNS configuration
See Azure Machine Learning custom DNS article for the DNS forwarding configurations.
If you need to configure custom DNS server without DNS forwarding, use the following patterns for the required A records.
<AI-STUDIO-GUID>.workspace.<region>.cert.api.azureml.ms
<AI-PROJECT-GUID>.workspace.<region>.cert.api.azureml.ms
<AI-STUDIO-GUID>.workspace.<region>.api.azureml.ms
<AI-PROJECT-GUID>.workspace.<region>.api.azureml.ms
ml-<workspace-name, truncated>-<region>-<AI-STUDIO-GUID>.<region>.notebooks.azure.net
ml-<workspace-name, truncated>-<region>-<AI-PROJECT-GUID>.<region>.notebooks.azure.net
Note
The workspace name for this FQDN might be truncated. Truncation is done to keep
ml-<workspace-name, truncated>-<region>-<workspace-guid>
at 63 characters or less.<instance-name>.<region>.instances.azureml.ms
Note
- Compute instances can be accessed only from within the virtual network.
- The IP address for this FQDN is not the IP of the compute instance. Instead, use the private IP address of the workspace private endpoint (the IP of the
*.api.azureml.ms
entries.)
<instance-name>.<region>.instances.azureml.ms
- Only used by theaz ml compute connect-ssh
command to connect to computes in a managed virtual network. Not needed if you are not using a managed network or SSH connections.<managed online endpoint name>.<region>.inference.ml.azure.com
- Used by managed online endpoints
To find the private IP addresses for your A records, see the Azure Machine Learning custom DNS article. To check AI-PROJECT-GUID, go to the Azure portal, select your project, settings, properties, and the workspace ID is displayed.
Limitations
- Private Azure AI Services and Azure AI Search aren't supported.
- The "Add your data" feature in the Azure AI Studio playground doesn't support private storage account.
- You might encounter problems trying to access the private endpoint for your hub if you're using Mozilla Firefox. This problem might be related to DNS over HTTPS in Mozilla Firefox. We recommend using Microsoft Edge or Google Chrome.