Merging changes synced from https://github.com/MicrosoftDocs/azure-docs-pr (branch live)

Author: Learn Build Service GitHub App

.openpublishing.redirection.json

@@ -3043,6 +3043,11 @@
             "redirect_url": "/sql/sql-server/stretch-database/stretch-database-databases-and-tables-stretch-database-advisor",
             "redirect_document_id": false
         },
+        {
+            "source_path_from_root": "/articles/dms/tutorial-azure-postgresql-to-azure-postgresql-online-portal.md",
+            "redirect_url": "/azure/postgresql/migrate/migration-service/tutorial-migration-service-single-to-flexible",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/vs-azure-tools-access-private-azure-clouds-with-visual-studio.md",
             "redirect_url": "/visualstudio/azure/vs-azure-tools-access-private-azure-clouds-with-visual-studio",
@@ -4089,7 +4094,27 @@
             "redirect_document_id": false
         },
         {
-            "source_path_from_root": "/articles/data-factory/continuous-integration-delivery-automate-github-actions.md",
+            "source_path_from_root": "/articles/openshift/tutorial-create-cluster.md",
+            "redirect_url": "/azure/openshift/create-cluster",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/openshift/tutorial-connect-cluster.md",
+            "redirect_url": "/azure/openshift/connect-cluster",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/openshift/tutorial-delete-cluster.md",
+            "redirect_url": "/azure/openshift/delete-cluster",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/openshift/quickstart-portal.md",
+            "redirect_url": "/azure/openshift/create-cluster",
+            "redirect_document_id": false
+        },
+        {
+           "source_path_from_root": "/articles/data-factory/continuous-integration-delivery-automate-github-actions.md",
             "redirect_url": "/azure",
             "redirect_document_id": false
         }

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

@@ -1,7 +1,7 @@
 ---
 title: Analyze a shelf image using pretrained models
 titleSuffix: Azure AI services
-description: Use the Product Understanding API to analyze a shelf image and receive rich product data.
+description: Use the Product Recognition API to analyze a shelf image and receive rich product data.
 author: PatrickFarley
 manager: nitinme
 ms.service: azure-ai-vision
@@ -13,7 +13,7 @@ ms.custom: build-2023, build-2023-dataai
 
 # Shelf Product Recognition (preview): Analyze shelf images using pretrained model
 
-The fastest way to start using Product Recognition is to use the built-in pretrained AI models. With the Product Understanding API, you can upload a shelf image and get the locations of products and gaps.
+The fastest way to start using Product Recognition is to use the built-in pretrained AI models. With the Product Recognition API, you can upload a shelf image and get the locations of products and gaps.
 
 :::image type="content" source="../media/shelf/shelf-analysis-pretrained.png" alt-text="Photo of a retail shelf with products and gaps highlighted with rectangles.":::
 
@@ -51,62 +51,50 @@ To analyze a shelf image, do the following steps:
 
 ## Examine the response
 
-A successful response is returned in JSON. The product understanding API results are returned in a `ProductUnderstandingResultApiModel` JSON field:
+A successful response is returned in JSON. The product recognition API results are returned in a `ProductRecognitionResultApiModel` JSON field:
 
 ```json
-{
-  "imageMetadata": {
-    "width": 2000,
-    "height": 1500
-  },
-  "products": [
-    {
-      "id": "string",
-      "boundingBox": {
-        "x": 1234,
-        "y": 1234,
-        "w": 12,
-        "h": 12
-      },
-      "classifications": [
-        {
-          "confidence": 0.9,
-          "label": "string"
-        }
-      ]
-    }
+"ProductRecognitionResultApiModel": {
+  "description": "Results from the product understanding operation.",
+  "required": [
+    "gaps",
+    "imageMetadata",
+    "products"
   ],
-  "gaps": [
-    {
-      "id": "string",
-      "boundingBox": {
-        "x": 1234,
-        "y": 1234,
-        "w": 123,
-        "h": 123
-      },
-      "classifications": [
-        {
-          "confidence": 0.8,
-          "label": "string"
-        }
-      ]
+  "type": "object",
+  "properties": {
+    "imageMetadata": {
+      "$ref": "#/definitions/ImageMetadataApiModel"
+    },
+    "products": {
+      "description": "Products detected in the image.",
+      "type": "array",
+      "items": {
+        "$ref": "#/definitions/DetectedObject"
+      }
+    },
+    "gaps": {
+      "description": "Gaps detected in the image.",
+      "type": "array",
+      "items": {
+        "$ref": "#/definitions/DetectedObject"
+      }
     }
-  ]
+  }
 }
 ```
 
 See the following sections for definitions of each JSON field.
 
-### Product Understanding Result API model
+### Product Recognition Result API model
 
-Results from the product understanding operation.
+Results from the product recognition operation.
 
 | Name | Type | Description | Required |
 | ---- | ---- | ----------- | -------- |
 | `imageMetadata` | [ImageMetadataApiModel](#image-metadata-api-model) | The image metadata information such as height, width and format. | Yes |
-| `products` |[DetectedObjectApiModel](#detected-object-api-model) | Products detected in the image. | Yes |
-| `gaps` | [DetectedObjectApiModel](#detected-object-api-model) | Gaps detected in the image. | Yes |
+| `products` |[DetectedObject](#detected-object-api-model) | Products detected in the image. | Yes |
+| `gaps` | [DetectedObject](#detected-object-api-model) | Gaps detected in the image. | Yes |
 
 ### Image Metadata API model
 
@@ -124,8 +112,8 @@ Describes a detected object in an image.
 | Name | Type | Description | Required |
 | ---- | ---- | ----------- | -------- |
 | `id` | string | ID of the detected object. | No |
-| `boundingBox` | [BoundingBoxApiModel](#bounding-box-api-model) | A bounding box for an area inside an image. | Yes |
-| `classifications` | [ImageClassificationApiModel](#image-classification-api-model) | Classification confidences of the detected object. | Yes |
+| `boundingBox` | [BoundingBox](#bounding-box-api-model) | A bounding box for an area inside an image. | Yes |
+| `tags` | [TagsApiModel](#image-tags-api-model) | Classification confidences of the detected object. | Yes |
 
 ### Bounding Box API model
 
@@ -138,18 +126,18 @@ A bounding box for an area inside an image.
 | `w` | integer | Width measured from the top-left point of the area, in pixels. | Yes |
 | `h` | integer | Height measured from the top-left point of the area, in pixels. | Yes |
 
-### Image Classification API model
+### Image Tags API model
 
 Describes the image classification confidence of a label.
 
 | Name | Type | Description | Required |
 | ---- | ---- | ----------- | -------- |
 | `confidence` | float | Confidence of the classification prediction. | Yes |
-| `label` | string | Label of the classification prediction. | Yes |
+| `name` | string | Label of the classification prediction. | Yes |
 
 ## Next steps
 
-In this guide, you learned how to make a basic analysis call using the pretrained Product Understanding REST API. Next, learn how to use a custom Product Recognition model to better meet your business needs.
+In this guide, you learned how to make a basic analysis call using the pretrained Product Recognition REST API. Next, learn how to use a custom Product Recognition model to better meet your business needs.
 
 > [!div class="nextstepaction"]
 > [Train a custom model for Product Recognition](../how-to/shelf-model-customization.md)

articles/ai-services/computer-vision/overview-image-analysis.md

@@ -142,11 +142,6 @@ To use the Image Analysis APIs, you must create your Azure AI Vision resource in
 | Japan East |  ✅ |  |  | ✅ | |
 
 
-
-### Query rates
-
-tbd
-
 ## Data privacy and security
 
 As with all of the Azure AI services, developers using the Azure AI Vision service should be aware of Microsoft's policies on customer data. See the [Azure AI services page](https://www.microsoft.com/trustcenter/cloudservices/cognitiveservices) on the Microsoft Trust Center to learn more.

articles/ai-services/openai/how-to/quota.md

@@ -7,7 +7,7 @@ author: mrbullwinkle
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 05/31/2024
+ms.date: 06/18/2024
 ms.author: mbullwin
 ---
 
@@ -91,6 +91,9 @@ As each request is received, Azure OpenAI computes an estimated max processed-to
 
 As requests come into the deployment endpoint, the estimated max-processed-token count is added to a running token count of all requests that is reset each minute. If at any time during that minute, the TPM rate limit value is reached, then further requests will receive a 429 response code until the counter resets.
 
+> [!IMPORTANT]
+> The token count used in the rate limit calculation is an estimate based in part on the character count of the API request. The rate limit token estimate is not the same as the token calculation that is used for billing/determining that a request is below a model's input token limit. Due to the approximate nature of the rate limit token calculation, it is expected behavior that a rate limit can be triggered prior to what might be expected in comparison to an exact token count measurement for each request.  
+
 RPM rate limits are based on the number of requests received over time. The rate limit expects that requests be evenly distributed over a one-minute period. If this average flow isn't maintained, then requests may receive a 429 response even though the limit isn't met when measured over the course of a minute. To implement this behavior, Azure OpenAI Service evaluates the rate of incoming requests over a small period of time, typically 1 or 10 seconds. If the number of requests received during that time exceeds what would be expected at the set RPM limit, then new requests will receive a 429 response code until the next evaluation period. For example, if Azure OpenAI is monitoring request rate on 1-second intervals, then rate limiting will occur for a 600-RPM deployment if more than 10 requests are received during each 1-second period (600 requests per minute = 10 requests per second).
 
 ### Rate limit best practices

articles/ai-services/openai/includes/model-matrix/provisioned-models.md

@@ -5,15 +5,15 @@ description: PTU-managed model availability by region.
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: include
-ms.date: 06/11/2024
+ms.date: 06/18/2024
 ---
 
 | **Region**     | **gpt-4**, **0613**   | **gpt-4**, **1106-Preview**   | **gpt-4**, **0125-Preview**   | **gpt-4**, **turbo-2024-04-09**   | **gpt-4o**, **2024-05-13**   | **gpt-4-32k**, **0613**   | **gpt-35-turbo**, **1106**   | **gpt-35-turbo**, **0125**   |
 |:-------------------|:-------------------:|:---------------------------:|:---------------------------:|:-------------------------------:|:--------------------------:|:-----------------------:|:--------------------------:|:--------------------------:|
-| australiaeast      | ✅                | ✅                        | ✅                        | ✅                            | -                      | ✅                    | ✅                       | ✅                       |
+| australiaeast      | ✅                | ✅                        | ✅                        | ✅                            | ✅                       | ✅                    | ✅                       | ✅                       |
 | brazilsouth        | ✅                | ✅                        | ✅                        | -                           | -                      | ✅                    | ✅                       | -                      |
 | canadacentral      | ✅                | -                       | -                       | -                           | -                      | ✅                    | -                      | ✅                       |
-| canadaeast         | ✅                | ✅                        | -                       | ✅                            | -                      | -                   | ✅                       | -                      |
+| canadaeast         | ✅                | ✅                        | -                       | ✅                            | ✅                       | -                   | ✅                       | -                      |
 | eastus             | ✅                | ✅                        | ✅                        | ✅                            | -                      | ✅                    | ✅                       | ✅                       |
 | eastus2            | ✅                | ✅                        | ✅                        | ✅                            | -                      | ✅                    | ✅                       | ✅                       |
 | francecentral      | ✅                | ✅                        | ✅                        | -                           | -                      | ✅                    | -                      | ✅                       |

articles/ai-services/openai/whats-new.md

@@ -10,7 +10,7 @@ ms.custom:
   - ignite-2023
   - references_regions
 ms.topic: whats-new
-ms.date: 06/13/2024
+ms.date: 06/18/2024
 recommendations: false
 ---
 
@@ -28,7 +28,7 @@ This article provides a summary of the latest releases and major documentation u
 
 * GPT-4o is now also available in:
     -  Sweden Central for standard regional deployment.
-    -  Japan East, Korea Central, Sweden Central, Switzerland North, & West US 3 for provisioned deployment.
+    -  Australia East, Canada East, Japan East, Korea Central, Sweden Central, Switzerland North, & West US 3 for provisioned deployment.
 
 For the latest information on model availability, see the [models page](./concepts/models.md).
 

articles/ai-studio/concepts/content-filtering.md

@@ -35,42 +35,43 @@ The content filtering models have been trained and tested on the following langu
 
 ## Create a content filter
 
-## How to create a content filter? 
 For any model deployment in [Azure AI Studio](https://ai.azure.com), you can directly use the default content filter, but you might want to have more control. For example, you could make a filter stricter or more lenient, or enable more advanced capabilities like prompt shields and protected material detection.
 
 Follow these steps to create a content filter:
 
-1. Go to [AI Studio](https://ai.azure.com) and select a project.
-1. Select **Content filters** from the left pane and then select **+ New content filter**.
+1. Go to [AI Studio](https://ai.azure.com) and navigate to your hub. Then select the **Content filters** tab on the left nav, and select the **Create content filter** button.
 
     :::image type="content" source="../media/content-safety/content-filter/create-content-filter.png" alt-text="Screenshot of the button to create a new content filter." lightbox="../media/content-safety/content-filter/create-content-filter.png":::
 
 1. On the **Basic information** page, enter a name for your content filter. Select a connection to associate with the content filter. Then select **Next**.
 
     :::image type="content" source="../media/content-safety/content-filter/create-content-filter-basic.png" alt-text="Screenshot of the option to select or enter basic information such as the filter name when creating a content filter." lightbox="../media/content-safety/content-filter/create-content-filter-basic.png":::
 
-1. On the **Input filters** page, you can set the filter for the input prompt. For example, you can enable prompt shields for jailbreak attacks. Then select **Next**.
+1. On the **Input filters** page, you can set the filter for the input prompt. Set the action and severity level threshold for each filter type. You configure both the default filters and other filters (like Prompt Shields for jailbreak attacks) on this page. Then select **Next**.
 
     :::image type="content" source="../media/content-safety/content-filter/configure-threshold.png" alt-text="Screenshot of the option to select input filters when creating a content filter." lightbox="../media/content-safety/content-filter/configure-threshold.png":::
 
     Content will be annotated by category and blocked according to the threshold you set. For the violence, hate, sexual, and self-harm categories, adjust the slider to block content of high, medium, or low severity.
 
-1. On the **Output filters** page, you can set the filter for the output completion. For example, you can enable filters for protected material detection. Then select **Next**. 
+1. On the **Output filters** page, you can configure the output filter, which will be applied to all output content generated by your model. Configure the individual filters as before. This page also provides the Streaming mode option, which lets you filter content in near-real-time as it's generated by the model, reducing latency. When you're finished select **Next**. 
 
     Content will be annotated by each category and blocked according to the threshold. For violent content, hate content, sexual content, and self-harm content category, adjust the threshold to block harmful content with equal or higher severity levels.
 
-1. Optionally, on the **Deployment** page, you can associate the content filter with a deployment. You can also associate the content filter with a deployment later. Then select **Create**.
+1. Optionally, on the **Deployment** page, you can associate the content filter with a deployment. If a selected deployment already has a filter attached, you must confirm that you want to replace it. You can also associate the content filter with a deployment later. Select **Create**.
 
     :::image type="content" source="../media/content-safety/content-filter/create-content-filter-deployment.png" alt-text="Screenshot of the option to select a deployment when creating a content filter." lightbox="../media/content-safety/content-filter/create-content-filter-deployment.png":::
 
     Content filtering configurations are created at the hub level in AI Studio. Learn more about configurability in the [Azure OpenAI docs](/azure/ai-services/openai/how-to/content-filters).
 
 1. On the **Review** page, review the settings and then select **Create filter**.
 
+### Use a blocklist as a filter
+
+You can apply a blocklist as either an input or output filter, or both. Enable the **Blocklist** option on the **Input filter** and/or **Output filter** page. Select one or more blocklists from the dropdown, or use the built-in profanity blocklist. You can combine multiple blocklists into the same filter.
 
-## How to apply a content filter?
+## Apply a content filter
 
-A default content filter is set when you create a deployment. You can also apply your custom content filter to your deployment. 
+The filter creation process gives you the option to apply the filter to the deployments you want. You can also change or remove content filters from your deployments at any time.
 
 Follow these steps to apply a content filter to a deployment:
 
@@ -83,11 +84,7 @@ Follow these steps to apply a content filter to a deployment:
 
     :::image type="content" source="../media/content-safety/content-filter/apply-content-filter.png" alt-text="Screenshot of apply content filter." lightbox="../media/content-safety/content-filter/apply-content-filter.png":::
 
-Now, you can go to the playground to test whether the content filter works as expected!
-
-## Content filtering categories and configurability
-
-You can apply a blocklist as either an input or output filter, or both. Enable the **Blocklist** option on the **Input filter** and/or **Output filter** page. Select one or more blocklists from the dropdown, or use the built-in profanity blocklist. You can combine multiple blocklists into the same filter.
+Now, you can go to the playground to test whether the content filter works as expected.
 
 ### Categories