Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into vnet-peer-update-2

Author: asudbring

.openpublishing.redirection.azure-monitor.json

@@ -6738,9 +6738,11 @@
             "source_path_from_root": "/articles/azure-monitor/essentials/pipeline-overview.md",
             "redirect_url": "/azure/azure-monitor/essentials/data-collection-rule-overview",
             "redirect_document_id": false
-        }
-        
-        
-
+        },
+        {
+            "source_path_from_root": "/articles/azure-monitor/agents/azure-monitor-agent-migration-tools.md",
+            "redirect_url": "/azure/azure-monitor/agents/azure-monitor-agent-migration",
+            "redirect_document_id": false
+        }     
     ]
 }

.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/how-to/working-with-models.md

@@ -4,7 +4,7 @@ titleSuffix: Azure OpenAI
 description: Learn about managing model deployment life cycle, updates, & retirement.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 10/04/2023
+ms.date: 06/18/2023
 ms.custom: references_regions, build-2023, build-2023-dataai, devx-track-azurepowershell
 manager: nitinme
 author: mrbullwinkle #ChrisHMSFT
@@ -75,7 +75,7 @@ There are three distinct model deployment upgrade options:
 |`NoAutoUpgrade` | The model deployment never automatically upgrades. Once the retirement date is reached the model deployment stops working. You need to update your code referencing that deployment to point to a nonexpired model deployment. |
 
 > [!NOTE]
-> `null` is equivalent to `AutoUpgradeWhenExpired`. If the **Version update policy** option is not present in the properties for a model that supports model upgrades this indicates the value is currently `null`. Once you explicitly modify this value, the property is visible in the studio properties page as well as via the REST API.
+> `null` is equivalent to `OnceCurrentVersionExpired`. If the **Version update policy** option is not present in the properties for a model that supports model upgrades this indicates the value is currently `null`. Once you explicitly modify this value, the property is visible in the studio properties page as well as via the REST API.
 
 ### Examples
 

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).