Merge branch 'main' into release-files-vaulted-backup

Author: v-alje

.openpublishing.redirection.sentinel.json

@@ -1055,6 +1055,21 @@
             "redirect_url": "/azure/sentinel/normalization-schema-process-event",
             "redirect_document_id": true
         },
+        {
+            "source_path_from_root": "/articles/sentinel/connect-cef-ama.md",
+            "redirect_url": "/azure/sentinel/connect-cef-syslog-ama",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/sentinel/connect-cef-syslog.md",
+            "redirect_url": "/azure/sentinel/connect-cef-syslog-ama",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/sentinel/connect-cef-syslog-options.md",
+            "redirect_url": "/azure/sentinel/connect-cef-syslog-ama",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/sentinel/notebooks-with-synapse.md",
             "redirect_url": "/azure/sentinel/notebooks-hunt",

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

@@ -16,7 +16,7 @@ Face Liveness detection can be used to determine if a face in an input video str
 
 The goal of liveness detection is to ensure that the system is interacting with a physically present live person at the time of authentication. Such systems have become increasingly important with the rise of digital finance, remote access control, and online identity verification processes.
 
-The liveness detection solution successfully defends against a variety of spoof types ranging from paper printouts, 2d/3d masks, and spoof presentations on phones and laptops. Liveness detection is an active area of research, with continuous improvements being made to counteract increasingly sophisticated spoofing attacks over time. Continuous improvements will be rolled out to the client and the service components over time as the overall solution gets more robust to new types of attacks.
+The liveness detection solution successfully defends against various spoof types ranging from paper printouts, 2d/3d masks, and spoof presentations on phones and laptops. Liveness detection is an active area of research, with continuous improvements being made to counteract increasingly sophisticated spoofing attacks over time. Continuous improvements will be rolled out to the client and the service components over time as the overall solution gets more robust to new types of attacks.
 
 [!INCLUDE [liveness-sdk-gate](../includes/liveness-sdk-gate.md)]
 
@@ -40,7 +40,7 @@ Once you have access to the SDK, follow instruction in the [azure-ai-vision-sdk]
 - For Swift iOS, follow the instructions in the [iOS sample](https://aka.ms/azure-ai-vision-face-liveness-client-sdk-ios-readme) 
 - For Kotlin/Java Android, follow the instructions in the [Android sample](https://aka.ms/liveness-sample-java) 
 
-Once you've added the code into your application, the SDK will handle starting the camera, guiding the end-user to adjust their position, composing the liveness payload, and calling the Azure AI Face cloud service to process the liveness payload.
+Once you've added the code into your application, the SDK handles starting the camera, guiding the end-user to adjust their position, composing the liveness payload, and calling the Azure AI Face cloud service to process the liveness payload.
 
 ### Orchestrate the liveness solution
 
@@ -86,7 +86,7 @@ The high-level steps involved in liveness orchestration are illustrated below:
 
 1. The SDK then starts the camera, guides the user to position correctly and then prepares the payload to call the liveness detection service endpoint. 
 
-1. The SDK calls the Azure AI Vision Face service to perform the liveness detection. Once the service responds, the SDK will notify the mobile application that the liveness check has been completed. 
+1. The SDK calls the Azure AI Vision Face service to perform the liveness detection. Once the service responds, the SDK notifies the mobile application that the liveness check has been completed. 
 
 1. The mobile application relays the liveness check completion to the app server. 
 
@@ -110,7 +110,7 @@ The high-level steps involved in liveness orchestration are illustrated below:
                 "method": "POST",
                 "contentLength": 352568,
                 "contentType": "multipart/form-data; boundary=--------------------------482763481579020783621915",
-                "userAgent": "PostmanRuntime/7.34.0"
+                "userAgent": ""
             },
             "response": {
                 "body": {
@@ -162,12 +162,12 @@ Use the following tips to ensure that your input images give the most accurate r
 #### Composition requirements:
 -	Photo is clear and sharp, not blurry, pixelated, distorted, or damaged. 
 -	Photo is not altered to remove face blemishes or face appearance.
--	Photo must be in an RGB color supported format (JPEG, PNG, WEBP, BMP). Recommended Face size is 200 pixels x 200 pixels. Face sizes larger than 200 pixels x 200 pixels will not result in better AI quality, and no larger than 6MB in size.
+-	Photo must be in an RGB color supported format (JPEG, PNG, WEBP, BMP). Recommended Face size is 200 pixels x 200 pixels. Face sizes larger than 200 pixels x 200 pixels will not result in better AI quality, and no larger than 6 MB in size.
 -	User is not wearing glasses, masks, hats, headphones, head coverings, or face coverings. Face should be free of any obstructions.
 -	Facial jewelry is allowed provided they do not hide your face. 
 -	Only one face should be visible in the photo.
 -	Face should be in neutral front-facing pose with both eyes open, mouth closed, with no extreme facial expressions or head tilt.
--	Face should be free of any shadows or red eyes. Please retake photo if either of these occur.
+-	Face should be free of any shadows or red eyes. Retake photo if either of these occur.
 -	Background should be uniform and plain, free of any shadows. 
 -	Face should be centered within the image and fill at least 50% of the image.
 
@@ -243,7 +243,7 @@ The high-level steps involved in liveness with verification orchestration are il
                 "method": "POST",
                 "contentLength": 352568,
                 "contentType": "multipart/form-data; boundary=--------------------------590588908656854647226496",
-                "userAgent": "PostmanRuntime/7.34.0"
+                "userAgent": ""
             },
             "response": {
                 "body": {

articles/ai-services/computer-vision/how-to/shelf-analyze.md

@@ -25,7 +25,7 @@ The fastest way to start using Product Recognition is to use the built-in pretra
 * Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesComputerVision"  title="create a Vision resource"  target="_blank">create a Vision resource</a> in the Azure portal. It must be deployed in the **East US** or **West US 2** region. After it deploys, select **Go to resource**.
   * You'll need the key and endpoint from the resource you create to connect your application to the Azure AI Vision service. You'll paste your key and endpoint into the code below later in the guide.
 * An Azure Storage resource with a blob storage container. [Create one](/azure/storage/common/storage-account-create?tabs=azure-portal)
-* [cURL](https://curl.haxx.se/) installed. Or, you can use a different REST platform, like Postman, Swagger, or the REST Client extension for VS Code.
+* [cURL](https://curl.haxx.se/) installed. Or, you can use a different REST platform, like Swagger or the [REST Client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) extension for VS Code.
 * A shelf image. You can download our [sample image](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/ComputerVision/shelf-analysis/shelf.png) or bring your own images. The maximum file size per image is 20 MB.
 
 ## Analyze shelf images

articles/ai-services/computer-vision/how-to/shelf-modify-images.md

@@ -24,7 +24,7 @@ This guide also shows you how to use the **Rectification API** to correct for pe
 * Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesComputerVision"  title="create a Vision resource"  target="_blank">create a Vision resource</a> in the Azure portal. It must be deployed in the **East US** or **West US 2** region. After it deploys, select **Go to resource**.
   * You'll need the key and endpoint from the resource you create to connect your application to the Azure AI Vision service. You'll paste your key and endpoint into the code below later in the quickstart.
 * An Azure Storage resource with a blob storage container. [Create one](/azure/storage/common/storage-account-create?tabs=azure-portal)
-* [cURL](https://curl.haxx.se/) installed. Or, you can use a different REST platform, like Postman, Swagger, or the REST Client extension for VS Code.
+* [cURL](https://curl.haxx.se/) installed. Or, you can use a different REST platform, like Swagger or the [REST Client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) extension for VS Code.
 * A set of photos that show adjacent parts of the same shelf. A 50% overlap between images is recommended. You can download and use the sample "unstitched" images from [GitHub](https://github.com/Azure-Samples/cognitive-services-sample-data-files/tree/master/ComputerVision/shelf-analysis).
 
 

articles/ai-services/computer-vision/how-to/shelf-planogram.md

@@ -22,7 +22,7 @@ A planogram is a diagram that indicates the correct placement of retail products
 
 ## Prerequisites
 * You must have already set up and run basic [Product Understanding analysis](./shelf-analyze.md) with the Product Understanding API.
-* [cURL](https://curl.haxx.se/) installed. Or, you can use a different REST platform, like Postman, Swagger, or the REST Client extension for VS Code.
+* [cURL](https://curl.haxx.se/) installed. Or, you can use a different REST platform, like Swagger or the [REST Client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) extension for VS Code.
 
 ## Prepare a planogram schema
 

articles/ai-services/containers/azure-kubernetes-recipe.md

@@ -191,7 +191,7 @@ The following steps are needed to get the required information to connect your c
 1. Get your container registry ID.
 
     ```azurecli-interactive
-    az acr show --resource-group cogserv-container-rg --name pattyregistry --query "id" --o table
+    az acr show --resource-group cogserv-container-rg --name pattyregistry --query "id" --output table
     ```
 
     Save the output for the scope parameter value, `<acrId>`, in the next step. It looks like:

articles/ai-services/custom-vision-service/copy-move-projects.md

@@ -14,7 +14,7 @@ ms.author: pafarley
 
 After you've created and trained a Custom Vision project, you may want to copy your project to another resource. If your app or business depends on a Custom Vision project, we recommend you copy your model to another Custom Vision account in another region. Then if a regional outage occurs, you can access your project in the region where it was copied.
 
-The **[ExportProject](https://westus2.dev.cognitive.microsoft.com/docs/services/Custom_Vision_Training_3.3/operations/5eb0bcc6548b571998fddeb3)** and **[ImportProject](https://westus2.dev.cognitive.microsoft.com/docs/services/Custom_Vision_Training_3.3/operations/5eb0bcc7548b571998fddee3)** APIs enable this scenario by allowing you to copy projects from one Custom Vision account into others. This guide shows you how to use these REST APIs with cURL. You can also use an HTTP request service like Postman to issue the requests.
+The **[ExportProject](https://westus2.dev.cognitive.microsoft.com/docs/services/Custom_Vision_Training_3.3/operations/5eb0bcc6548b571998fddeb3)** and **[ImportProject](https://westus2.dev.cognitive.microsoft.com/docs/services/Custom_Vision_Training_3.3/operations/5eb0bcc7548b571998fddee3)** APIs enable this scenario by allowing you to copy projects from one Custom Vision account into others. This guide shows you how to use these REST APIs with cURL. You can also use an HTTP request service, like the [REST Client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) for Visual Studio Code, to issue the requests.
 
 > [!TIP]
 > For an example of this scenario using the Python client library, see the [Move Custom Vision Project](https://github.com/Azure-Samples/custom-vision-move-project/tree/master/) repository on GitHub.

articles/ai-services/custom-vision-service/storage-integration.md

@@ -17,7 +17,7 @@ You can integrate your Custom Vision project with an Azure blob storage queue to
 
 You can also use Azure storage to store backup copies of your published models.
 
-This guide shows you how to use these REST APIs with cURL. You can also use an HTTP request service like Postman to make the requests.
+This guide shows you how to use these REST APIs with cURL. You can also use an HTTP request service, like the [REST Client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) for Visual Studio Code, to make the requests.
 
 > [!NOTE]
 > Push notifications depend on the optional _notificationQueueUri_ parameter in the **CreateProject** API, and model backups require that you also use the optional _exportModelContainerUri_ parameter. This guide will use both for the full set of features.

articles/ai-services/openai/concepts/models.md

@@ -4,7 +4,7 @@ titleSuffix: Azure OpenAI
 description: Learn about the different model capabilities that are available with Azure OpenAI.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 02/21/2024
+ms.date: 03/06/2024
 ms.custom: references_regions, build-2023, build-2023-dataai, refefences_regions
 manager: nitinme
 author: mrbullwinkle #ChrisHMSFT
@@ -110,11 +110,11 @@ GPT-4 version 0125-preview is an updated version of the GPT-4 Turbo preview prev
 | `gpt-4-32k`(0314)  | 32,768               | Sep 2021         |
 | `gpt-4` (0613)     | 8,192                | Sep 2021         |
 | `gpt-4-32k` (0613) | 32,768               | Sep 2021         |
-| `gpt-4` (1106-preview)**<sup>1</sup>**<br>**GPT-4 Turbo Preview** | Input: 128,000  <br> Output: 4,096           | Apr 2023         |
-| `gpt-4` (0125-preview)**<sup>1</sup>**<br>**GPT-4 Turbo Preview** | Input: 128,000  <br> Output: 4,096           | Dec 2023         |
+| `gpt-4` (1106-Preview)**<sup>1</sup>**<br>**GPT-4 Turbo Preview** | Input: 128,000  <br> Output: 4,096           | Apr 2023         |
+| `gpt-4` (0125-Preview)**<sup>1</sup>**<br>**GPT-4 Turbo Preview** | Input: 128,000  <br> Output: 4,096           | Dec 2023         |
 | `gpt-4` (vision-preview)**<sup>2</sup>**<br>**GPT-4 Turbo with Vision Preview**  | Input: 128,000  <br> Output: 4,096              | Apr 2023       |
 
-**<sup>1</sup>** GPT-4 Turbo Preview = `gpt-4` (0125-preview). To deploy this model, under **Deployments** select model **gpt-4**. For **Model version** select **0125-preview**. 
+**<sup>1</sup>** GPT-4 Turbo Preview = `gpt-4` (0125-Preview) or `gpt-4` (1106-Preview). To deploy this model, under **Deployments** select model **gpt-4**. Under version select (0125-Preview) or (1106-Preview).
 
 **<sup>2</sup>** GPT-4 Turbo with Vision Preview = `gpt-4` (vision-preview). To deploy this model, under **Deployments** select model **gpt-4**. For **Model version** select **vision-preview**.
 
@@ -132,8 +132,8 @@ GPT-4 version 0125-preview is an updated version of the GPT-4 Turbo preview prev
 |---|:---|:---|  
 | gpt-4 (0314) | | East US <br> France Central <br> South Central US <br> UK South |  
 | gpt-4 (0613) | Australia East <br> Canada East <br> France Central <br> Sweden Central <br> Switzerland North | East US <br> East US 2 <br> Japan East <br> UK South |  
-| gpt-4 (1106-preview) | Australia East <br> Canada East <br> East US 2 <br> France Central <br> Norway East <br> South India <br> Sweden Central <br> UK South <br> West US | |  
-| gpt-4 (0125-preview) | East US <br> North Central US <br> South Central US <br> |
+| gpt-4 (1106-Preview) | Australia East <br> Canada East <br> East US 2 <br> France Central <br> Norway East <br> South India <br> Sweden Central <br> UK South <br> West US | |  
+| gpt-4 (0125-Preview) | East US <br> North Central US <br> South Central US <br> |
 | gpt-4 (vision-preview) |  Sweden Central <br> West US <br> Japan East <br> Switzerland North <br> Australia East|   |  
 
 #### Azure Government regions
@@ -142,7 +142,7 @@ The following GPT-4 models are available with [Azure Government](/azure/azure-go
 
 |Model ID | Model Availability |
 |--|--|
-| `gpt-4` (1106-preview) | US Gov Virginia<br>US Gov Arizona |
+| `gpt-4` (1106-Preview) | US Gov Virginia<br>US Gov Arizona |
 
 
 ### GPT-3.5 models

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

@@ -6,7 +6,7 @@ description: Learn how to create your own customized model with Azure OpenAI Ser
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: include
-ms.date: 10/10/2023
+ms.date: 03/06/2024
 author: mrbullwinkle
 ms.author: mbullwin
 ---
@@ -34,6 +34,7 @@ The following models support fine-tuning:
 - `davinci-002`
 - `gpt-35-turbo` (0613)
 - `gpt-35-turbo` (1106)
+- `gpt-35-turbo` (0125)
 
 Or you can fine tune a previously fine-tuned model, formatted as base-model.ft-{jobid}.