Merge branch 'main' into release-ai-speech-cnv-foundry

Author: v-alje

articles/ai-foundry/how-to/azure-policy.md

@@ -61,7 +61,13 @@ You can also assign policies by using [Azure PowerShell](/azure/governance/polic
 
 ## Conditional access policies
 
-To control who can access your Azure AI Foundry hubs and projects, use [Microsoft Entra Conditional Access](/azure/active-directory/conditional-access/overview). To use Conditional Access for hubs, [assign the Conditional Access policy](/azure/active-directory/conditional-access/concept-conditional-access-cloud-apps) to the app named __Azure Machine Learning__. The app ID is __0736f41a-0425-bdb5-1563eff02385__. 
+To control who can access your Azure AI Foundry hubs and projects, use [Microsoft Entra Conditional Access](/azure/active-directory/conditional-access/overview). To use Conditional Access for hubs, [assign the Conditional Access policy](/azure/active-directory/conditional-access/concept-conditional-access-cloud-apps) to the following apps:
+
+| App name | App ID | Description |
+|---|---|---|
+| Azure AI Foundry App | cb2ff863-7f30-4ced-ab89-a00194bcf6d9 | Use to control access to the Azure AI Foundry portal. |
+| Azure Machine Learning Web App | d7304df8-741f-47d3-9bc2-df0e24e2071f | Use to control access to Azure Machine Learning studio. |
+| Azure Machine Learning | 0736f41a-0425-bdb5-1563eff02385 | Use to control direct access to the Azure Machine Learning API. For example, when using the SDK or REST API. Azure AI Foundry hub based projects rely on the Azure Machine Learning API. |
 
 ## Configure built-in policies
 

articles/ai-foundry/how-to/develop/connections-add-sdk.md

@@ -42,15 +42,13 @@ There are various authentication methods for the different connection types. Whe
 
 ## Azure OpenAI in Foundry Models
 
-The following example creates an Azure OpenAI in Azure AI Foundry Models connection.
+The following example uses the [AzureOpenAIConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.azureopenaiconnection) class to create an Azure OpenAI in Azure AI Foundry Models connection.
 
 > [!TIP]
 > To connect to Azure OpenAI and more AI services with one connection, you can use the [AI services connection](#azure-ai-services) instead.
 
 ```python
-from azure.ai.ml.entities import AzureOpenAIConnection, ApiKeyConfiguration
-from azure.ai.ml.entities import UsernamePasswordConfiguration
-
+from azure.ai.ml.entities import AzureOpenAIConnection
 name = "XXXXXXXXX"
 
 target = "https://XXXXXXXXX.cognitiveservices.azure.com/"
@@ -59,9 +57,6 @@ resource_id= "Azure-resource-id"
 
 # Microsoft Entra ID
 credentials = None
-# Uncomment the following if you need to use API key instead
-# api_key= "my-key"
-# credentials = ApiKeyConfiguration(key=api_key)
 
 wps_connection = AzureOpenAIConnection(
     name=name,
@@ -75,22 +70,19 @@ ml_client.connections.create_or_update(wps_connection)
 
 ## Azure AI services
 
-The following example creates an Azure AI services connection. This example creates one connection for the AI services documented in the [Connect to Azure AI services](../../../ai-services/connect-services-ai-foundry-portal.md) article. The same connection also supports Azure OpenAI.
+The following example uses the [AzureAIServicesConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.azureaiservicesconnection) class to create an Azure AI services connection. This example creates one connection for the AI services documented in the [Connect to Azure AI services](../../../ai-services/connect-services-ai-foundry-portal.md) article. The same connection also supports Azure OpenAI.
 
 ```python
-from azure.ai.ml.entities import AzureAIServicesConnection, ApiKeyConfiguration
-from azure.ai.ml.entities import UsernamePasswordConfiguration
+from azure.ai.ml.entities import AzureAIServicesConnection
 
 name = "my-ai-services"
 
-target = "https://XXXXXXXXX.cognitiveservices.azure.com/"
+target = "https://my.cognitiveservices.azure.com/"
 resource_id=""
 
 # Microsoft Entra ID
 credentials = None
-# Uncomment the following if you need to use API key instead
-# api_key= "my-key"
-# credentials = ApiKeyConfiguration(key=api_key)
+
 
 wps_connection = AzureAIServicesConnection(
     name=name,
@@ -103,20 +95,17 @@ ml_client.connections.create_or_update(wps_connection)
 
 ## Azure AI Search
 
-The following example creates an Azure AI Search connection:
+The following example uses the [AzureAISearchConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.azureaisearchconnection) class to create an Azure AI Search connection:
 
 ```python
-from azure.ai.ml.entities import AzureAISearchConnection, ApiKeyConfiguration
-from azure.ai.ml.entities import UsernamePasswordConfiguration
+from azure.ai.ml.entities import AzureAISearchConnection
 
 name = "my_aisearch_demo_connection"
-target = "https://XXXXXXXXX.search.windows.net"
+target = "https://my.search.windows.net"
 
 # Microsoft Entra ID
 credentials = None
-# Uncomment the following if you need to use API key instead
-# api_key= "my-key"
-# credentials = ApiKeyConfiguration(key=api_key)
+
 
 wps_connection = AzureAISearchConnection(
     name=name,
@@ -132,11 +121,10 @@ The following example creates an Azure AI Content Safety connection:
 
 ```python
 from azure.ai.ml.entities import AzureContentSafetyConnection, ApiKeyConfiguration
-from azure.ai.ml.entities import UsernamePasswordConfiguration
 
 name = "my_content_safety"
 
-target = "https://XXXXXXXXX.cognitiveservices.azure.com/"
+target = "https://my.cognitiveservices.azure.com/"
 api_key = "XXXXXXXXX"
 
 wps_connection = AzureContentSafetyConnection(
@@ -157,7 +145,7 @@ from azure.ai.ml.entities import ServerlessConnection
 
 name = "my_maas_apk"
 
-endpoint = "https://XXXXXXXXX.eastus2.inference.ai.azure.com/"
+endpoint = "https://my.eastus2.inference.ai.azure.com/"
 api_key = "XXXXXXXXX"
 wps_connection = ServerlessConnection(
     name=name,
@@ -170,11 +158,10 @@ ml_client.connections.create_or_update(wps_connection)
 
 ## Azure Blob Storage
 
-The following example creates an Azure Blob Storage connection. This connection is authenticated with an account key or a SAS token:
+The following example uses the [AzureBlobStoreConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.azureblobstoreconnection) class to create an Azure Blob Storage connection. This connection is authenticated with an account key or a SAS token:
 
 ```python
 from azure.ai.ml.entities import AzureBlobStoreConnection, SasTokenConfiguration,AccountKeyConfiguration
-from azure.ai.ml.entities import UsernamePasswordConfiguration
 
 
 name = "my_blobstore"
@@ -198,7 +185,7 @@ The following example creates Azure Data Lake Storage Gen 2 connection. This con
 
 ```python
 from azure.ai.ml.entities import WorkspaceConnection
-from azure.ai.ml.entities import UsernamePasswordConfiguration, ServicePrincipalConfiguration
+from azure.ai.ml.entities import ServicePrincipalConfiguration
 
 sp_config = ServicePrincipalConfiguration(
     tenant_id="XXXXXXXXXXXX",
@@ -222,7 +209,7 @@ ml_client.connections.create_or_update(workspace_connection=wps_connection)
 
 ## Microsoft OneLake
 
-The following example creates a Microsoft OneLake connection. This connection is authenticated with a Service Principal:
+The following example uses the [MicrosoftOneLakeWorkspaceConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.microsoftonelakeconnection) class to create a Microsoft OneLake connection. This connection is authenticated with a Service Principal:
 
 ```python
 from azure.ai.ml.entities import MicrosoftOneLakeWorkspaceConnection, OneLakeArtifact
@@ -254,7 +241,7 @@ ml_client.connections.create_or_update(workspace_connection=wps_connection)
 
 ## Serp
 
-The following example creates a Serp connection:
+The following example uses the [SerpConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.serpconnection) class:
 
 ```python
 from azure.ai.ml.entities import SerpConnection
@@ -271,7 +258,7 @@ ml_client.connections.create_or_update(wps_connection)
 
 ## OpenAI
 
-The following example creates an OpenAI (not Azure OpenAI) connection:
+The following example uses the [OpenAIConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.openaiconnection) class to create an OpenAI (not Azure OpenAI) connection:
 
 ```python
 from azure.ai.ml.entities import OpenAIConnection
@@ -288,11 +275,11 @@ ml_client.connections.create_or_update(wps_connection)
 
 ## Custom
 
-The following example creates custom connection:
+The following example uses the [ApiKeyConfiguration](/python/api/azure-ai-ml/azure.ai.ml.entities.apikeyconnection) class to create custom connection:
 
 ```python
 from azure.ai.ml.entities import WorkspaceConnection
-from azure.ai.ml.entities import UsernamePasswordConfiguration, ApiKeyConfiguration
+from azure.ai.ml.entities import ApiKeyConfiguration
 
 
 name = "my_custom"

articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md

@@ -3,7 +3,7 @@ title:  Understanding costs associated with provisioned throughput units (PTU)
 description: Learn about provisioned throughput costs and billing in Azure OpenAI. 
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 03/31/2025
+ms.date: 05/20/2025
 manager: nitinme
 author: aahill 
 ms.author: aahi 
@@ -47,10 +47,6 @@ Quota for provisioned deployments shows up in Azure AI Foundry as the following
 
 :::image type="content" source="../media/provisioned/ptu-quota-page.png" alt-text="Screenshot of quota UI for Azure OpenAI provisioned." lightbox="../media/provisioned/ptu-quota-page.png":::
 
-
-> [!NOTE]
-> Global provisioned and data zone provisioned deployments are only supported for gpt-4o and gpt-4o-mini models at this time. For more information on model availability, review the [models documentation](../concepts/models.md).
-
 ## Hourly usage
 
 Provisioned, Data Zone Provisioned, and Global Provisioned deployments are charged an hourly rate ($/PTU/hr) on the number of PTUs that have been deployed.  For example, a 300 PTU deployment will be charged the hourly rate times 300.  All Azure OpenAI pricing is available in the Azure Pricing Calculator.