Merge pull request #2180 from MicrosoftDocs/main

1/7/2025 AM Publish

articles/ai-studio/how-to/model-catalog-overview.md

@@ -165,7 +165,7 @@ Pay-per-token billing is available only to users whose Azure subscription belong
 
 ### Network isolation for models deployed via serverless APIs
 
-Managed computes for models deployed as serverless APIs follow the public network access flag setting of the Azure AI Foundry hub that has the project in which the deployment exists. To help secure your managed compute, disable the public network access flag on your Azure AI Foundry hub. You can help secure inbound communication from a client to your managed compute by using a private endpoint for the hub.
+Endpoints for models deployed as serverless APIs follow the public network access flag setting of the Azure AI Foundry hub that has the project in which the deployment exists. To help secure your serverless API endpoint, disable the public network access flag on your Azure AI Foundry hub. You can help secure inbound communication from a client to your endpoint by using a private endpoint for the hub.
 
 To set the public network access flag for the Azure AI Foundry hub:
 
@@ -177,11 +177,11 @@ To set the public network access flag for the Azure AI Foundry hub:
 
 #### Limitations
 
-* If you have an Azure AI Foundry hub with a managed compute created before July 11, 2024, managed computes added to projects in this hub won't follow the networking configuration of the hub. Instead, you need to create a new managed compute for the hub and create new serverless API deployments in the project so that the new deployments can follow the hub's networking configuration.
+* If you have an Azure AI Foundry hub with a private endpoint created before July 11, 2024, serverless API endpoints added to projects in this hub won't follow the networking configuration of the hub. Instead, you need to create a new private endpoint for the hub and create new serverless API deployments in the project so that the new deployments can follow the hub's networking configuration.
 
-* If you have an Azure AI Foundry hub with MaaS deployments created before July 11, 2024, and you enable a managed compute on this hub, the existing MaaS deployments won't follow the hub's networking configuration. For serverless API deployments in the hub to follow the hub's networking configuration, you need to create the deployments again.
+* If you have an Azure AI Foundry hub with MaaS deployments created before July 11, 2024, and you enable a private endpoint on this hub, the existing serverless API deployments won't follow the hub's networking configuration. For serverless API deployments in the hub to follow the hub's networking configuration, you need to create the deployments again.
 
-* Currently, [Azure OpenAI On Your Data](/azure/ai-services/openai/concepts/use-your-data) support isn't available for MaaS deployments in private hubs, because private hubs have the public network access flag disabled.
+* Currently, [Azure OpenAI On Your Data](/azure/ai-services/openai/concepts/use-your-data) support isn't available for serverless API deployments in private hubs, because private hubs have the public network access flag disabled.
 
 * Any network configuration change (for example, enabling or disabling the public network access flag) might take up to five minutes to propagate.
 

articles/machine-learning/azure-machine-learning-release-notes-cli-v2.md

@@ -24,6 +24,17 @@ In this article, learn about Azure Machine Learning CLI (v2) releases.
 __RSS feed__: Get notified when this page is updated by copying and pasting the following URL into your feed reader:
 `https://learn.microsoft.com/api/search/rss?search=%22Azure+machine+learning+release+notes-v2%22&locale=en-us`
 
+## 2024-12-17
+
+### Azure Machine Learning CLI (v2) v2.33.0
+
+- `az ml workspace create --provision-network-now`
+  - Added `--provision-network-now` property to trigger the provisioning of the managed network when creating a workspace with the managed network enabled, or else it does nothing.
+- `az ml capability-host`
+  - Added create operation.
+  - Added get operation.
+  - Added delete operation.
+
 ## 2024-09-18
 
 ### Azure Machine Learning CLI (v2) v2.30.0

articles/machine-learning/concept-model-lifecycle-retirement.md

@@ -22,7 +22,7 @@ Models in the model catalog are continually refreshed with newer and more capabl
 > This article describes deprecation and retirement only for models that can be deployed to __serverless APIs__, not managed compute. To learn more about the differences between deployment to serverless APIs and managed computes, see [Model Catalog and Collections](concept-model-catalog.md).
 
 > [!NOTE]
-> Azure OpenAI models in the model catalog are provided through Azure OpenAI Service. For information about Azure Open AI model deprecation and retirement, see the [Azure OpenAI service product documentation](/azure/ai-services/openai/concepts/model-retirements).
+> Azure OpenAI models in the model catalog are provided through Azure OpenAI Service. For information about Azure OpenAI model deprecation and retirement, see the [Azure OpenAI service product documentation](/azure/ai-services/openai/concepts/model-retirements).
 
 ## Model lifecycle stages
 
@@ -72,4 +72,4 @@ Models labeled _Retired_ are no longer available for use. You can't create new d
 ## Related content
 
 - [Model Catalog and Collections](concept-model-catalog.md)
-- [Data, privacy, and security for use of models through the Model Catalog](concept-data-privacy.md)
+- [Data, privacy, and security for use of models through the Model Catalog](concept-data-privacy.md)

articles/search/includes/quickstarts/java.md

@@ -5,7 +5,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: include
-ms.date: 11/01/2024
+ms.date: 01/07/2025
 ---
 
 Build a Java console application using the [Azure.Search.Documents](/java/api/overview/azure/search) library to create, load, and query a search index. 

articles/search/includes/quickstarts/python-semantic.md

@@ -106,7 +106,6 @@ semantic_config = SemanticConfiguration(
 # Create the semantic settings with the configuration
 semantic_search = SemanticSearch(configurations=[semantic_config])
 
-semantic_settings = SemanticSearch(configurations=[semantic_config])
 scoring_profiles = []
 suggester = [{'name': 'sg', 'source_fields': ['Tags', 'Address/City', 'Address/Country']}]
 

articles/search/search-get-started-portal-import-vectors.md

@@ -193,12 +193,12 @@ The wizard supports Azure AI Vision image retrieval through multimodal embedding
 
 1. Make sure your Azure AI Search service is in the same region.
 
-1. After the service is deployed, go to the resource and select **Access control** to assign the **Cognitive Services OpenAI User** role to your search service's managed identity. Optionally, you can use key-based authentication for the connection.
+1. After the service is deployed, go to the resource and select **Access control** to assign the **Cognitive Services User** role to your search service's managed identity. Optionally, you can use key-based authentication for the connection.
 
 After you finish these steps, you should be able to select the Azure AI Vision vectorizer in the **Import and vectorize data** wizard.
 
 > [!NOTE]
-> If you can't select an Azure AI Vision vectorizer, make sure you have an Azure AI Vision resource in a supported region. Also make sure that your search service's managed identity has **Cognitive Services OpenAI User** permissions.
+> If you can't select an Azure AI Vision vectorizer, make sure you have an Azure AI Vision resource in a supported region. Also make sure that your search service's managed identity has **Cognitive Services User** permissions.
 
 ### [Azure AI Foundry model catalog](#tab/model-catalog)
 
@@ -331,7 +331,7 @@ Chunking is built in and nonconfigurable. The effective settings are:
 
 1. Specify whether you want your search service to authenticate using an API key or managed identity.
 
-   + The identity should have a **Cognitive Services OpenAI User** role on the Azure AI multi-services account.
+   + The identity should have a **Cognitive Services User** role on the Azure AI multi-services account.
 
 1. Select the checkbox that acknowledges the billing effects of using these resources.
 

articles/search/search-get-started-text.md

@@ -14,13 +14,15 @@ ms.custom:
   - devx-track-python
   - ignite-2023
 ms.topic: quickstart
-ms.date: 11/01/2024
+ms.date: 01/07/2025
 ---
 
 # Quickstart: Full text search using the Azure SDKs
 
 Learn how to use the *Azure.Search.Documents* client library in an Azure SDK to create, load, and query a search index using sample data for [full text search](search-lucene-query-architecture.md). Full text search uses Apache Lucene for indexing and queries, and a BM25 ranking algorithm for scoring results.
 
+This quickstart creates and queries a small hotels-quickstart index containing data about 4 hotels.
+
 This quickstart has steps for the following SDKs:
 
 + [Azure SDK for .NET](?tabs=dotnet#create-load-and-query-an-index)

articles/search/search-limits-quotas-capacity.md

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 12/09/2024
+ms.date: 01/07/2025
 ms.custom:
   - references_regions
   - build-2024
@@ -69,7 +69,7 @@ Maximum number of documents per index are:
 + 288 billion on L1
 + 576 billion on L2
 
-Each instance of a complex collection counts as a separate document in terms of these limits.
+You can check the number of documents in the Azure portal and through REST calls that include `search=*` and `count=true`.
 
 Maximum size of each document is approximately 16 megabytes. Document size is actually a limit on the size of the indexing API request payload, which is 16 megabytes. That payload can be a single document, or a batch of documents. For a batch with a single document, the maximum document size is 16 MB of JSON.