Merge pull request #2178 from MicrosoftDocs/repo_sync_working_branch

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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/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.