Merge pull request #2427 from MicrosoftDocs/main

1/22/2025 AM Publish

Author: Taojunshen

articles/ai-services/computer-vision/tutorials/liveness.md

@@ -505,7 +505,7 @@ To deploy between the same subscription, but different regions you would just ha
 
 ### Cross tenant deployment
 
-The accounts used to generate access tokens with `az account get-access-token --tenant` should have Cognitive Services OpenAI Contributor permissions to the associated Azure OpenAI resources.
+The account used to generate access tokens with `az account get-access-token --tenant` should have Cognitive Services OpenAI Contributor permissions to both the source and destination Azure OpenAI resources. You will need to generate two different tokens, one for the source tenant and one for the destination tenant.
 
 ```python
 import requests

articles/ai-services/openai/includes/fine-tuning-python.md

@@ -324,7 +324,7 @@ To deploy between the same subscription, but different regions, you would just h
 
 ### Cross tenant deployment
 
-The accounts used to generate access tokens with `az account get-access-token --tenant` should have Cognitive Services OpenAI Contributor permissions to the associated Azure OpenAI resources.
+The account used to generate access tokens with `az account get-access-token --tenant` should have Cognitive Services OpenAI Contributor permissions to both the source and destination Azure OpenAI resources. You will need to generate two different tokens, one for the source tenant and one for the destination tenant.
 
 
 ```bash

articles/ai-services/openai/includes/fine-tuning-rest.md

@@ -69,6 +69,7 @@ The limits in this table apply per Speech resource when you create a custom spee
 | Quota | Free (F0) | Standard (S0) |
 |-----|-----|-----|
 | REST API limit | 100 requests per 10 seconds (600 requests per minute) | 100 requests per 10 seconds (600 requests per minute) |
+| Max number of custom model deployments per Speech resource | 1 | 50 |
 | Max number of speech datasets | 2 | 500 |
 | Max acoustic dataset file size for data import | 2 GB | 2 GB |
 | Max language dataset file size for data import | 200 MB | 1.5 GB |

articles/ai-services/speech-service/speech-services-quotas-and-limits.md

@@ -34,7 +34,7 @@ In this article, you learn how to create and develop your first prompt flow in A
 - If you don't have an Azure AI Foundry project already, first [create a project](create-projects.md).
 - Prompt flow requires a compute session. If you don't have a runtime, you can [create one in Azure AI Foundry portal](./create-manage-compute-session.md).
 - You need a deployed model.
-- In your project, configure access control for the blog storage account. Assign the **Storage Blob Data Contributor** role to your user account.
+- In your project, configure access control for the blob storage account. Assign the **Storage Blob Data Contributor** role to your user account.
     - In the bottom left of the Azure AI Foundry portal, select **Management center**.
     - In **Connected resources** for your project, select the link that corresponds to the **Azure Blob Storage** type. 
     - Select **View in Azure Portal**

articles/ai-studio/how-to/flow-develop.md

@@ -6,22 +6,22 @@ ms.service: azure-machine-learning
 ms.subservice: mldata
 ms.topic: how-to
 author: fbsolo-ms1 
-ms.author: franksolomon 
-ms.reviewer: yogipandey
-ms.date: 12/06/2023
+ms.author: franksolomon
+ms.reviewer: franksolomon
+ms.date: 01/22/2025
 ms.custom: template-concept
 ---
 
 # Feature retrieval specification and usage in training and inference
 
 This article describes the feature retrieval specification, and how to use a feature retrieval specification in training and inference.
 
-A feature retrieval specification is an artifact that defines a list of features to use in model input. The features in a feature retrieval specification:
+A feature retrieval specification is an artifact that defines a list of features to use in model input. In a feature retrieval specification, the features:
 
 - must exist in a feature set registered in a feature store
 - can exist in multiple feature sets and multiple feature stores
 
-The feature retrieval specification is used at the time of model training and the time of model inference. These flow steps involve the specification:
+The feature retrieval specification is used at the time of model training and at the time of model inference. These flow steps involve the specification:
 
 1. Select features, and generate a feature retrieval specification
 1. Use that specification and observation data to generate training data resource with a [point-in-time join](./offline-retrieval-point-in-time-join-concepts.md)
@@ -69,7 +69,7 @@ featurestore1.generate_feature_retrieval_spec("./feature_retrieval_spec_folder",
 
 ```
 
-Find detailed examples in the **2.Experiment-train-models-using-features.ipynb** notebook, hosted at [this resource](https://github.com/Azure/azureml-examples/tree/main/sdk/python/featurestore_sample/notebooks/sdk_only).
+The **2.Experiment-train-models-using-features.ipynb** notebook, hosted at [this resource](https://github.com/Azure/azureml-examples/tree/main/sdk/python/featurestore_sample/notebooks/sdk_only), has detailed examples.
 
 The function generates a YAML file artifact, which has a structure similar to the structure in this example:
 ```yaml
@@ -122,7 +122,7 @@ training_df = get_offline_features(
 
 ```
 
-The second option sets the feature retrieval specification as an input to the built-in feature retrieval (pipeline) component. It combines that feature retrieval specification with other inputs - for example, the observation data set. It then submits an Azure Machine Learning pipeline (Spark) job, to generate the training data set as output. This option is recommended to make the training pipeline ready for production, for repeated runs. For more details about the built-in feature retrieval (pipeline) component, visit the [feature retrieval component](#built-in-feature-retrieval-component) resource.
+The second option sets the feature retrieval specification as an input to the built-in feature retrieval (pipeline) component. It combines that feature retrieval specification with other inputs - for example, the observation data set. It then submits an Azure Machine Learning pipeline (Spark) job, to generate the training data set as output. This option is recommended as a way to make the training pipeline ready for production, for repeated runs. Visit the [feature retrieval component](#built-in-feature-retrieval-component) resource for more information about the built-in feature retrieval (pipeline) components.
 
 ## Package a feature retrieval specification with model artifact
 

articles/machine-learning/feature-retrieval-concepts.md

@@ -39,7 +39,6 @@ Once collection is enabled, the data you collect helps you:
 > The MDC feature is incompatible with any other image than Ubuntu 18.04, which is no available after the Ubuntu 18.04 image is deprecated.
 >
 > mMore information you can refer to:
-> * [openmpi3.1.2-ubuntu18.04 release-notes](https://github.com/Azure/AzureML-Containers/blob/master/base/cpu/openmpi3.1.2-ubuntu18.04/release-notes.md)
 > * [data science virtual machine release notes](../data-science-virtual-machine/release-notes.md#september-20-2022)
 
 >[!NOTE]

articles/machine-learning/v1/how-to-enable-data-collection.md

@@ -15,22 +15,22 @@ ms.custom:
 ms.date: 12/10/2024
 ---
 
-# Index data from Azure Database for MySQL flexible server
+# Index data from Azure Database for MySQL Flexible Server
 
 > [!IMPORTANT]
 > MySQL support is currently in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). You can use 2020-06-30-preview or later to index your content. We recommend the latest preview API. There is currently no portal support.
 
 In this article, learn how to configure an [**indexer**](search-indexer-overview.md) that imports content from Azure Database for MySQL and makes it searchable in Azure AI Search. Inputs to the indexer are your row, in a single table or view. Output is a search index with searchable content in individual fields.
 
-This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information that's specific to indexing from Azure Database for MySQL flexible server. It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.
+This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information that's specific to indexing from Azure Database for MySQL Flexible Server. It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.
 
 When configured to include a high water mark and soft deletion, the indexer takes all changes, uploads, and deletes for your MySQL database. It reflects these changes in your search index. Data extraction occurs when you submit the Create Indexer request.
 
 ## Prerequisites
 
 - [Register for the preview](https://aka.ms/azure-cognitive-search/indexer-preview) to provide scenario feedback. You can access the feature automatically after form submission.
 
-- [Azure Database for MySQL flexible server](/azure/mysql/flexible-server/overview) and sample data. Data must reside in a table or view. A primary key is required. If you're using a view, it must have a [high water mark column](#DataChangeDetectionPolicy). 
+- [Azure Database for MySQL Flexible Server](/azure/mysql/flexible-server/overview) and sample data. Data must reside in a table or view. A primary key is required. If you're using a view, it must have a [high water mark column](#DataChangeDetectionPolicy). 
 
 - Read permissions. A *full access* connection string includes a key that grants access to the content, but if you're using Azure roles, make sure the [search service managed identity](search-howto-managed-identities-data-sources.md) has **Reader** permissions on MySQL.
 

articles/search/search-howto-index-mysql.md

@@ -7,7 +7,7 @@ author: diberry
 ms.author: diberry
 ms.service: azure-ai-search
 ms.topic: tutorial
-ms.date: 01/17/2025
+ms.date: 01/21/2025
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet
@@ -34,9 +34,11 @@ The function app authenticates through the SDK to the cloud-based Azure AI Searc
 
 ## Azure Function: Search the catalog
 
-The [Search API](https://github.com/Azure-Samples/azure-search-static-web-app/blob/main/api/Search.cs) takes a search term and searches across the documents in the search index, returning a list of matches. 
+The [Search API](https://github.com/Azure-Samples/azure-search-static-web-app/blob/main/api/Search.cs) takes a search term and searches across the documents in the search index, returning a list of matches. Through the Suggest API, partial strings are sent to the search engine as the user types, suggesting search terms such as book titles and authors across the documents in the search index, and returning a small list of matches. 
 
-The Azure function pulls in the search configuration information, and fulfills the query.
+The Azure function pulls in the search configuration information, and fulfills the query. 
+
+The search suggester, `sg`, is defined in the [schema file](https://github.com/Azure-Samples/azure-search-static-web-app/blob/main/bulk-insert/BookSearchIndex.cs) used during bulk upload.
 
 :::code language="csharp" source="~/azure-search-static-web-app/api/Search.cs" :::
 
@@ -46,17 +48,9 @@ Call the Azure Function in the React client with the following code.
 
 :::code language="csharp" source="~/azure-search-static-web-app/client/src/pages/Search/Search.js" :::
 
-## Azure Function: Suggestions from the catalog
-
-The [Suggest API](https://github.com/Azure-Samples/azure-search-static-web-app/blob/main/api/Suggest.cs) takes a search term while a user is typing and suggests search terms such as book titles and authors across the documents in the search index, returning a small list of matches. 
-
-The search suggester, `sg`, is defined in the [schema file](https://github.com/Azure-Samples/azure-search-static-web-app/blob/main/bulk-insert/BookSearchIndex.cs) used during bulk upload.
-
-:::code language="csharp" source="~/azure-search-static-web-app/api/Suggest.cs"  :::
-
 ## Client: Suggestions from the catalog
 
-The Suggest function API is called in the React app at `\client\src\components\SearchBar\SearchBar.js` as part of component initialization:
+The Suggest function API is called in the React app at `\client\src\components\SearchBar\SearchBar.js` as part of the [Material UI's Autocomplete component](https://mui.com/material-ui/react-autocomplete/). This component uses the input text to search for authors and books that match the input text then displays those possible matches at selectable items in the drop-down list. 
 
 :::code language="csharp" source="~/azure-search-static-web-app/client/src/components/SearchBar/SearchBar.js" :::