Merge pull request #276608 from MicrosoftDocs/main

5/29/2024 AM Publish

Author: Taojunshen

.openpublishing.redirection.azure-kubernetes-service.json

@@ -80,6 +80,11 @@
             "redirect_url": "/azure/aks/azure-csi-files-storage-provision",
             "redirect_document_id": false
         },
+        {
+            "source_path_from_root": "/articles/aks/learn/tutorial-kubernetes-workload-identity.md",
+            "redirect_url": "/azure/aks/workload-identity-deploy-cluster",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/aks/workload-identity-migration-sidecar.md",
             "redirect_url": "/azure/aks/workload-identity-migrate-from-pod-identity",

articles/ai-services/computer-vision/concept-face-recognition-data-structures.md

@@ -19,10 +19,6 @@ ms.author: pafarley
 
 This article explains the data structures used in the Face service for face recognition operations. These data structures hold data on faces and persons.
 
-You can try out the capabilities of face recognition quickly and easily using Vision Studio.
-> [!div class="nextstepaction"]
-> [Try Vision Studio](https://portal.vision.cognitive.azure.com/)
-
 [!INCLUDE [Gate notice](./includes/identity-gate-notice.md)]
 
 ## Data structures used with Identify 

articles/ai-services/computer-vision/concept-face-recognition.md

@@ -19,10 +19,6 @@ ms.author: pafarley
 
 This article explains the concept of Face recognition, its related operations, and the underlying data structures. Broadly, face recognition is the process of verifying or identifying individuals by their faces. Face recognition is important in implementing the identification scenario, which enterprises and apps can use to verify that a (remote) user is who they claim to be.
 
-You can try out the capabilities of face recognition quickly and easily using Vision Studio.
-> [!div class="nextstepaction"]
-> [Try Vision Studio](https://portal.vision.cognitive.azure.com/)
-
 
 ## Face recognition operations
 

articles/ai-services/computer-vision/how-to/identity-access-token.md

@@ -33,9 +33,9 @@ If the ISV learns that a client is using the LimitedAccessToken for non-approved
 
 ## Prerequisites
 
-* [cURL](https://curl.haxx.se/) installed (or another tool that can make HTTP requests).
-* The ISV needs to have either an [Azure AI Face](https://ms.portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/Face) resource or an [Azure AI services multi-service](https://ms.portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/AllInOne) resource.
-* The client needs to have an [Azure AI Face](https://ms.portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/Face) resource.
+* [cURL](https://curl.se/) installed (or another tool that can make HTTP requests).
+* The ISV needs to have either an [Azure AI Face](https://portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/Face) resource or an [Azure AI services multi-service](https://portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/AllInOne) resource.
+* The client needs to have an [Azure AI Face](https://portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/Face) resource.
 
 ## Step 1: ISV obtains client's Face resource ID
 

articles/ai-services/computer-vision/how-to/specify-detection-model.md

@@ -42,7 +42,7 @@ The different face detection models are optimized for different tasks. See the f
 |---------|------------|-------------------|-------------|--|
 |**detection_01** | Default choice for all face detection operations. | Not optimized for small, side-view, or blurry faces.                                                     | Returns main face attributes (head pose, age, emotion, and so on) if they're specified in the detect call. | Returns face landmarks if they're specified in the detect call. |
 |**detection_02** | Released in May 2019 and available optionally in all face detection operations.   | Improved accuracy on small, side-view, and blurry faces.  | Does not return face attributes. | Does not return face landmarks.     |
-|**detection_03** | Released in February 2021 and available optionally in all face detection operations.                   | Further improved accuracy, including on smaller faces (64x64 pixels) and rotated face orientations.       | Returns mask and head pose attributes if they're specified in the detect call. | Returns face landmarks if they're specified in the detect call. |
+|**detection_03** | Released in February 2021 and available optionally in all face detection operations.                   | Further improved accuracy, including on smaller faces (64x64 pixels) and rotated face orientations.       | Returns mask, blur, and head pose attributes if they're specified in the detect call. | Returns face landmarks if they're specified in the detect call. |
 
 
 The best way to compare the performances of the detection models is to use them on a sample dataset. We recommend calling the [Detect] API on a variety of images, especially images of many faces or of faces that are difficult to see, using each detection model. Pay attention to the number of faces that each model returns.
@@ -64,7 +64,7 @@ A request URL for the [Detect] REST API will look like this:
 If you are using the client library, you can assign the value for `detectionModel` by passing in an appropriate string. If you leave it unassigned, the API will use the default model version (`detection_01`). See the following code example for the .NET client library.
 
 ```csharp
-string imageUrl = "https://news.microsoft.com/ceo/assets/photos/06_web.jpg";
+string imageUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/detection1.jpg";
 var faces = await faceClient.Face.DetectWithUrlAsync(url: imageUrl, returnFaceId: false, returnFaceLandmarks: false, recognitionModel: "recognition_04", detectionModel: "detection_03");
 ```
 
@@ -81,7 +81,7 @@ await faceClient.PersonGroup.CreateAsync(personGroupId, "My Person Group Name",
 
 string personId = (await faceClient.PersonGroupPerson.CreateAsync(personGroupId, "My Person Name")).PersonId;
 
-string imageUrl = "https://news.microsoft.com/ceo/assets/photos/06_web.jpg";
+string imageUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/detection1.jpg";
 await client.PersonGroupPerson.AddFaceFromUrlAsync(personGroupId, personId, imageUrl, detectionModel: "detection_03");
 ```
 
@@ -97,7 +97,7 @@ You can also specify a detection model when you add a face to an existing **Face
 ```csharp
 await faceClient.FaceList.CreateAsync(faceListId, "My face collection", recognitionModel: "recognition_04");
 
-string imageUrl = "https://news.microsoft.com/ceo/assets/photos/06_web.jpg";
+string imageUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/detection1.jpg";
 await client.FaceList.AddFaceFromUrlAsync(faceListId, imageUrl, detectionModel: "detection_03");
 ```
 

articles/ai-services/computer-vision/how-to/use-large-scale.md

@@ -92,13 +92,13 @@ private static async Task TrainLargeFaceList(
     int timeIntervalInMilliseconds = 1000)
 {
     // Trigger a train call.
-    await FaceClient.LargeTrainLargeFaceListAsync(largeFaceListId);
+    await FaceClient.LargeFaceList.TrainAsync(largeFaceListId);
 
     // Wait for training finish.
     while (true)
     {
-        Task.Delay(timeIntervalInMilliseconds).Wait();
-        var status = await faceClient.LargeFaceList.TrainAsync(largeFaceListId);
+        await Task.Delay(timeIntervalInMilliseconds);
+        var status = await faceClient.LargeFaceList.GetTrainingStatusAsyn(largeFaceListId);
 
         if (status.Status == Status.Running)
         {
@@ -123,7 +123,7 @@ Previously, a typical use of **FaceList** with added faces and **FindSimilar** l
 const string FaceListId = "myfacelistid_001";
 const string FaceListName = "MyFaceListDisplayName";
 const string ImageDir = @"/path/to/FaceList/images";
-faceClient.FaceList.CreateAsync(FaceListId, FaceListName).Wait();
+await faceClient.FaceList.CreateAsync(FaceListId, FaceListName);
 
 // Add Faces to the FaceList.
 Parallel.ForEach(
@@ -141,7 +141,7 @@ const string QueryImagePath = @"/path/to/query/image";
 var results = new List<SimilarPersistedFace[]>();
 using (Stream stream = File.OpenRead(QueryImagePath))
 {
-    var faces = faceClient.Face.DetectWithStreamAsync(stream).Result;
+    var faces = await faceClient.Face.DetectWithStreamAsync(stream);
     foreach (var face in faces)
     {
         results.Add(await faceClient.Face.FindSimilarAsync(face.FaceId, FaceListId, 20));
@@ -156,7 +156,7 @@ When migrating it to **LargeFaceList**, it becomes the following:
 const string LargeFaceListId = "mylargefacelistid_001";
 const string LargeFaceListName = "MyLargeFaceListDisplayName";
 const string ImageDir = @"/path/to/FaceList/images";
-faceClient.LargeFaceList.CreateAsync(LargeFaceListId, LargeFaceListName).Wait();
+await faceClient.LargeFaceList.CreateAsync(LargeFaceListId, LargeFaceListName);
 
 // Add Faces to the LargeFaceList.
 Parallel.ForEach(
@@ -178,7 +178,7 @@ const string QueryImagePath = @"/path/to/query/image";
 var results = new List<SimilarPersistedFace[]>();
 using (Stream stream = File.OpenRead(QueryImagePath))
 {
-    var faces = faceClient.Face.DetectWithStreamAsync(stream).Result;
+    var faces = await faceClient.Face.DetectWithStreamAsync(stream);
     foreach (var face in faces)
     {
         results.Add(await faceClient.Face.FindSimilarAsync(face.FaceId, largeFaceListId: LargeFaceListId));

articles/ai-services/computer-vision/how-to/use-persondirectory.md

@@ -56,7 +56,7 @@ var client = new HttpClient();
 // Request headers
 client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", "{subscription key}");
 
-var addPersonUri = "https:// {endpoint}/face/v1.0-preview/persons";
+var addPersonUri = "https://{endpoint}/face/v1.0-preview/persons";
 
 HttpResponseMessage response;
 
@@ -113,10 +113,7 @@ Stopwatch s = Stopwatch.StartNew();
 string status = "notstarted";
 do
 {
-    if (status == "succeeded")
-    {
-        await Task.Delay(500);
-    }
+    await Task.Delay(500);
 
     var operationResponseMessage = await client.GetAsync(operationLocation);
 

articles/ai-services/content-safety/overview.md

@@ -134,8 +134,7 @@ Feel free to [contact us](mailto:[email protected]) if you need
 
 ### Query rates
 
-#### Moderation APIs
-| Pricing Tier | Requests per 10 seconds |
+| Pricing Tier | Requests per 10 seconds  |
 | :----------- | :--------------------- |
 | F0           | 1000                     |
 | S0           | 1000                     |

articles/ai-services/content-safety/quickstart-groundedness.md

@@ -18,8 +18,8 @@ Follow this guide to use Azure AI Content Safety Groundedness detection to check
 ## Prerequisites
 
 * An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services/) 
-* Once you have your Azure subscription, <a href="https://aka.ms/acs-create"  title="Create a Content Safety resource"  target="_blank">create a Content Safety resource </a> in the Azure portal to get your key and endpoint. Enter a unique name for your resource, select your subscription, and select a resource group, supported region (East see [Region availability](/azure/ai-services/content-safety/overview#region-availability)), and supported pricing tier. Then select **Create**.
-   * The resource takes a few minutes to deploy. After it does, go to the new resource. In the left pane, under **Resource Management**, select **API Keys and Endpoints**. Copy one of the subscription key values and endpoint to a temporary location for later use.
+* Once you have your Azure subscription, <a href="https://aka.ms/acs-create"  title="Create a Content Safety resource"  target="_blank">create a Content Safety resource </a> in the Azure portal to get your key and endpoint. Enter a unique name for your resource, select your subscription, and select a resource group, supported region (East US, East US2, West US, Sweden Central), and supported pricing tier. Then select **Create**.
+* The resource takes a few minutes to deploy. After it does, go to the new resource. In the left pane, under **Resource Management**, select **API Keys and Endpoints**. Copy one of the subscription key values and endpoint to a temporary location for later use.
 * (Optional) If you want to use the _reasoning_ feature, create an Azure OpenAI Service resource with a GPT model deployed.
 * [cURL](https://curl.haxx.se/) or [Python](https://www.python.org/downloads/) installed.
 
@@ -55,7 +55,7 @@ This section walks through a sample request with cURL. Paste the command below i
     }'
     ```
 
-1. Open a command prompt and run the cURL command.
+Open a command prompt and run the cURL command.
 
 
 #### [Python](#tab/python)
@@ -132,7 +132,7 @@ The parameters in the request body are defined in this table:
 | - `query`       | (Optional) This represents the question in a QnA task. Character limit: 7,500. | String  |
 | **text**   | (Required) The LLM output text to be checked. Character limit: 7,500. |  String  |
 | **groundingSources**  | (Required) Uses an array of grounding sources to validate AI-generated text. Up to 55,000 characters of grounding sources can be analyzed in a single request. | String array    |
-| **reasoning**  | (Optional) Specifies whether to use the reasoning feature. The default value is `false`. If `true`, you need to bring your own Azure OpenAI GPT-4 Turbo resources to provide an explanation. Be careful: using reasoning increases the processing time.| Boolean   |
+| **reasoning**  | (Optional) Specifies whether to use the reasoning feature. The default value is `false`. If `true`, you need to bring your own Azure OpenAI GPT-4 Turbo (1106-preview) resources to provide an explanation. Be careful: using reasoning increases the processing time.| Boolean   |
 
 ### Interpret the API response
 
@@ -161,14 +161,13 @@ The JSON objects in the output are defined here:
 
 ## Check groundedness with reasoning
 
-The Groundedness detection API provides the option to include _reasoning_ in the API response. With reasoning enabled, the response includes a `"reasoning"` field that details specific instances and explanations for any detected ungroundedness. Be careful: using reasoning increases the processing time and incurs extra fees. 
-
+The Groundedness detection API provides the option to include _reasoning_ in the API response. With reasoning enabled, the response includes a `"reasoning"` field that details specific instances and explanations for any detected ungroundedness.
 ### Bring your own GPT deployment
 
 > [!TIP]
-> At the moment, we only support **Azure OpenAI GPT-4 Turbo** resources and do not support other GPT types. Your GPT-4 Turbo resources can be deployed in any region; however, we recommend that they be located in the same region as the content safety resources to minimize potential latency.
+> At the moment, we only support **Azure OpenAI GPT-4 Turbo (1106-preview)** resources and do not support other GPT types. You have the flexibility to deploy your GPT-4 Turbo (1106-preview) resources in any region. However, to minimize potential latency and avoid any geographical boundary data privacy and risk concerns, we recommend situating them in the same region as your content safety resources. For comprehensive details on data privacy, please refer to the [Data, privacy and security guidelines for Azure OpenAI Service](/legal/cognitive-services/openai/data-privacy) and [Data, privacy, and security for Azure AI Content Safety](/legal/cognitive-services/content-safety/data-privacy?context=%2Fazure%2Fai-services%2Fcontent-safety%2Fcontext%2Fcontext).
 
-In order to use your Azure OpenAI GPT4-Turbo resource to enable the reasoning feature, use Managed Identity to allow your Content Safety resource to access the Azure OpenAI resource:
+In order to use your Azure OpenAI GPT4-Turbo (1106-preview) resource to enable the reasoning feature, use Managed Identity to allow your Content Safety resource to access the Azure OpenAI resource:
 
 [!INCLUDE [openai-account-access](includes/openai-account-access.md)]
 
@@ -281,8 +280,8 @@ The parameters in the request body are defined in this table:
 | **text**   | (Required) The LLM output text to be checked. Character limit: 7,500. |  String  |
 | **groundingSources**  | (Required) Uses an array of grounding sources to validate AI-generated text. Up to 55,000 characters of grounding sources can be analyzed in a single request. | String array    |
 | **reasoning**  | (Optional) Set to `true`, the service uses Azure OpenAI resources to provide an explanation. Be careful: using reasoning increases the processing time and incurs extra fees.| Boolean   |
-| **llmResource**  | (Required) If you want to use your own Azure OpenAI GPT4-Turbo resource to enable reasoning, add this field and include the subfields for the resources used. | String   |
-| - `resourceType `| Specifies the type of resource being used. Currently it only allows `AzureOpenAI`. We only support Azure OpenAI GPT-4 Turbo resources and do not support other GPT types. Your GPT-4 Turbo resources can be deployed in any region; however, we recommend that they be located in the same region as the content safety resources to minimize potential latency. | Enum|
+| **llmResource**  | (Required) If you want to use your own Azure OpenAI GPT4-Turbo (1106-preview) resource to enable reasoning, add this field and include the subfields for the resources used. | String   |
+| - `resourceType `| Specifies the type of resource being used. Currently it only allows `AzureOpenAI`. We only support Azure OpenAI GPT-4 Turbo (1106-preview) resources and do not support other GPT types. | Enum|
 | - `azureOpenAIEndpoint `| Your endpoint URL for Azure OpenAI service.  | String |
 | - `azureOpenAIDeploymentName` | The name of the specific GPT deployment to use. | String|
 

articles/ai-services/speech-service/toc.yml

@@ -572,6 +572,10 @@ items:
         - name: Text to speech REST API
           href: rest-text-to-speech.md
           displayName: reference, tts output, output format
+        - name: Custom voice REST API
+          href: /rest/api/speechapi/operation-groups?&preserve-view=true=rest-speechapi-2023-12-01-preview
+        - name: Batch synthesis REST API
+          href: /rest/api/batchtexttospeech/operation-groups?&preserve-view=true=rest-batchtexttospeech-2024-04-01
         - name: Speaker Recognition REST API
           href: /rest/api/speakerrecognition/
           displayName: reference