fix merge

Author: mattbriggs

.openpublishing.redirection.azure-monitor.json

@@ -6664,6 +6664,21 @@
             "redirect_url": "/azure/azure-monitor/monitor-azure-monitor-reference",
             "redirect_document_id": false
         },
+        {
+            "source_path_from_root": "/articles/azure-monitor/ai-ops/responsible-ai-faq.md",
+            "redirect_url": "/azure/copilot/overview",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/azure-monitor/ai-ops/investigator-overview.md",
+            "redirect_url": "/azure/copilot/overview",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/azure-monitor/ai-ops/investigate-alert-instance.md",
+            "redirect_url": "/azure/copilot/overview",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/azure-monitor/azure-monitor-monitoring-reference.md",
             "redirect_url": "/azure/azure-monitor/monitor-azure-monitor-reference",
@@ -6688,7 +6703,18 @@
             "source_path_from_root": "/articles/azure-monitor/agents/resource-manager-data-collection-rules.md",
             "redirect_url": "/azure/azure-monitor/essentials/data-collection-rule-create-edit?tabs=arm#manually-create-a-dcr",
             "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/azure-monitor/essentials/remote-write-prometheus.md",
+            "redirect_url": "/azure/azure-monitor/essentials/prometheus-remote-write-virtual-machines",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/azure-monitor/essentials/prometheus-get-started.md",
+            "redirect_url": "/azure/azure-monitor/essentials/prometheus-metrics-overview",
+            "redirect_document_id": false
         }
+        
 
     ]
 }

.openpublishing.redirection.json

@@ -3857,6 +3857,11 @@
         {
             "source_path_from_root": "/articles/orbital/overview-analytics.md",
             "redirect_url": "/azure/orbital/overview",
+            "redirect_document_id": false  
+        },
+        {
+            "source_path_from_root": "/articles/aks/intro-kubernetes.md",
+            "redirect_url": "/azure/aks/what-is-aks",
             "redirect_document_id": false
         },
         {

articles/advisor/advisor-how-to-calculate-total-cost-savings.md

@@ -1,11 +1,15 @@
 ---
-title: Export cost savings in Azure Advisor
+title: Calculate cost savings in Azure Advisor
 ms.topic: article
 ms.date: 02/06/2024
 description: Export cost savings in Azure Advisor and calculate the aggregated potential yearly savings by using the cost savings amount for each recommendation.
 ---
 
-# Export cost savings
+# Calculate cost savings
+
+This article provides guidance on how to calculate total cost savings in Azure Advisor. 
+
+## Export cost savings for recommendations
 
 To calculate aggregated potential yearly savings, follow these steps: 
 
@@ -21,5 +25,14 @@ The Advisor **Overview** page opens.
     [![Screenshot of the Azure Advisor cost recommendations page that shows download option.](./media/advisor-how-to-calculate-total-cost-savings.png)](./media/advisor-how-to-calculate-total-cost-savings.png#lightbox)
 
 > [!NOTE]
-> Recommendations show savings individually, and may overlap with the savings shown in other recommendations, for example – you can only benefit from savings plans for compute or reservations for virtual machines, but not from both.
+> Different types of cost savings recommendations are generated using overlapping datasets (for example, VM rightsizing/shutdown, VM reservations and savings plan recommendations all consider on-demand VM usage). As a result, resource changes (e.g., VM shutdowns) or reservation/savings plan purchases will impact on-demand usage, and the resulting recommendations and associated savings forecast. 
+
+## Understand cost savings
+
+Azure Advisor provides recommendations for resizing/shutting down underutilized resources, purchasing compute reserved instances, and savings plans for compute.
+
+These recommendations contain one or more calls-to-action and forecasted savings from following the recommendations. Recommendations should be followed in a specific order: rightsizing/shutdown, followed by reservation purchases, and finally, the savings plan purchase. This sequence allows each step to impact the subsequent ones positively.
+
+For example, rightsizing or shutting down resources reduces on-demand costs immediately. This change in your usage pattern essentially invalidates your existing reservation and savings plan recommendations, as they were based on your pre-rightsizing usage and costs. Updated reservation and savings plan recommendations (and their forecasted savings) should appear within three days. 
 
+The forecasted savings from reservations and savings plans are based on actual rates and usage, while the forecasted savings from rightsizing/shutdown are based on retail rates. The actual savings may vary depending on the usage patterns and rates. Assuming there are no material changes to your usage patterns, your actual savings from reservations and savings plan should be in line with the forecasts. Savings from rightsizing/shutdown vary based on your actual rates. This is important if you intend to track cost savings forecasts from Azure Advisor.

articles/advisor/toc.yml

@@ -74,7 +74,7 @@
     href: advisor-azure-resource-graph.md
   - name: Consume Advisor score 
     href: azure-advisor-score.md
-  - name: Export cost savings
+  - name: Calculate total cost savings
     href: advisor-how-to-calculate-total-cost-savings.md
 - name: Reference
   items:

articles/ai-services/document-intelligence/concept-accuracy-confidence.md

@@ -8,7 +8,7 @@ ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 02/29/2024
+ms.date: 04/16/2023
 ms.author: lajanuar
 ---
 
@@ -53,10 +53,11 @@ Field confidence indicates an estimated probability between 0 and 1 that the pre
 ## Interpret accuracy and confidence scores for custom models
 
 When interpreting the confidence score from a custom model, you should consider all the confidence scores returned from the model. Let's start with a list of all the confidence scores.
-1. **Document type confidence score**: The document type confidence is an indicator of closely the analyzed document resembleds documents in the training dataset. When the document type confidence is low, this is indicative of template or structural variations in the analyzed document. To improve the document type confidence, label a document with that specific variation and add it to your training dataset. Once the model is re-trained, it should be better equipped to handl that class of variations.
-2. **Field level confidence**: Each labled field extracted has an associated confidence score. This score reflects the model's confidence on the position of the value extracted. While evaluating the confidence you should also look at the underlying extraction confidence to generate a comprehensive confidence for the extracted result. Evaluate the OCR results for text extraction or selection marks depending on the field type to generate a composite confidence score for the field.
-3. **Word confidence score** Each word extracted within the document has an associated confidence score. The score represents the confidence of the transcription. The pages array contains an array of words, each word has an associated span and confidence. Spans from the custom field extracted values will match the spans of the extracted words.
-4. **Selection mark confidence score**: The pages array also contains an array of selection marks, each selection mark has a confidence score representing the confidence of the seletion mark and selection state detection. When a labeled field is a selection mark, the custom field selection confidence combined with the selection mark confidence is an accurate representation of the overall confidence that the field was extracted correctly.
+
+1. **Document type confidence score**: The document type confidence is an indicator of closely the analyzed document resembles documents in the training dataset. When the document type confidence is low, it's indicative of template or structural variations in the analyzed document. To improve the document type confidence, label a document with that specific variation and add it to your training dataset. Once the model is retrained, it should be better equipped to handle that class of variations.
+2. **Field level confidence**: Each labeled field extracted has an associated confidence score. This score reflects the model's confidence on the position of the value extracted. While evaluating confidence scores, you should also look at the underlying extraction confidence to generate a comprehensive confidence for the extracted result. Evaluate the `OCR` results for text extraction or selection marks depending on the field type to generate a composite confidence score for the field.
+3. **Word confidence score** Each word extracted within the document has an associated confidence score. The score represents the confidence of the transcription. The pages array contains an array of words and each word has an associated span and confidence score. Spans from the custom field extracted values match the spans of the extracted words.
+4. **Selection mark confidence score**: The pages array also contains an array of selection marks. Each selection mark has a confidence score representing the confidence of the selection mark and selection state detection. When a labeled field has a selection mark, the custom field selection combined with the selection mark confidence is an accurate representation of overall confidence accuracy.
 
 The following table demonstrates how to interpret both the accuracy and confidence scores to measure your custom model's performance.
 
@@ -69,7 +70,7 @@ The following table demonstrates how to interpret both the accuracy and confiden
 
 ## Table, row, and cell confidence
 
-With the addition of table, row and cell confidence with the ```2024-02-29-preview``` API, here are some common questions that should help with interpreting the table, row and cell scores:
+With the addition of table, row and cell confidence with the ```2024-02-29-preview``` API, here are some common questions that should help with interpreting the table, row, and cell scores:
 
 **Q:** Is it possible to see a high confidence score for cells, but a low confidence score for the row?<br>
 

articles/ai-services/document-intelligence/disaster-recovery.md

@@ -8,7 +8,7 @@ ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 03/06/2024
+ms.date: 04/17/2024
 ms.author: lajanuar
 ---
 
@@ -65,7 +65,7 @@ The process for copying a custom model consists of the following steps:
 The following HTTP request gets copy authorization from your target resource. You need to enter the endpoint and key of your target resource as headers.
 
 ```http
-POST https://<your-resource-name>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview
+POST https://<your-resource-endpoint>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview
 Ocp-Apim-Subscription-Key: {<your-key>}
 ```
 
@@ -96,7 +96,7 @@ You receive a `200` response code with response body that contains the JSON payl
 The following HTTP request starts the copy operation on the source resource. You need to enter the endpoint and key of your source resource as the url and header. Notice that the request URL contains the model ID of the source model you want to copy.
 
 ```http
-POST https://<your-resource-name>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview
+POST https://<your-resource-endpoint>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview
 Ocp-Apim-Subscription-Key: {<your-key>}
 ```
 
@@ -117,7 +117,7 @@ You receive a `202\Accepted` response with an Operation-Location header. This va
 
 ```http
 HTTP/1.1 202 Accepted
-Operation-Location: https://<your-resource-name>.cognitiveservices.azure.com/documentintelligence/operations/{operation-id}?api-version=2024-02-29-preview
+Operation-Location: https://<your-resource-endpoint>.cognitiveservices.azure.com/documentintelligence/operations/{operation-id}?api-version=2024-02-29-preview
 ```
 
 > [!NOTE]
@@ -126,7 +126,7 @@ Operation-Location: https://<your-resource-name>.cognitiveservices.azure.com/doc
 ## Track Copy progress
 
 ```console
-GET https://<your-resource-name>.cognitiveservices.azure.com/documentintelligence/operations/{<operation-id>}?api-version=2024-02-29-preview
+GET https://<your-resource-endpoint>.cognitiveservices.azure.com/documentintelligence/operations/{<operation-id>}?api-version=2024-02-29-preview
 Ocp-Apim-Subscription-Key: {<your-key>}
 ```
 
@@ -135,7 +135,7 @@ Ocp-Apim-Subscription-Key: {<your-key>}
 You can also use the **[Get model](/rest/api/aiservices/document-models/get-model?view=rest-aiservices-2023-07-31&preserve-view=true&tabs=HTTP)** API to track the status of the operation by querying the target model. Call the API using the target model ID that you copied down from the [Generate Copy authorization request](#generate-copy-authorization-request) response.
 
 ```http
-GET https://<your-resource-name>/documentintelligence/documentModels/{modelId}?api-version=2024-02-29-preview" -H "Ocp-Apim-Subscription-Key: <your-key>
+GET https://<your-resource-endpoint>/documentintelligence/documentModels/{modelId}?api-version=2024-02-29-preview" -H "Ocp-Apim-Subscription-Key: <your-key>
 ```
 
 In the response body, you see information about the model. Check the `"status"` field for the status of the model.
@@ -155,7 +155,7 @@ The following code snippets use cURL to make API calls. You also need to fill in
 **Request**
 
 ```bash
-curl -i -X POST "<your-resource-name>/documentintelligence/documentModels:authorizeCopy?api-version=2024-02-29-preview"
+curl -i -X POST "<your-resource-endpoint>/documentintelligence/documentModels:authorizeCopy?api-version=2024-02-29-preview"
 -H "Content-Type: application/json"
 -H "Ocp-Apim-Subscription-Key: <YOUR-KEY>"
 --data-ascii "{
@@ -182,7 +182,7 @@ curl -i -X POST "<your-resource-name>/documentintelligence/documentModels:author
 **Request**
 
 ```bash
-curl -i -X POST "<your-resource-name>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview"
+curl -i -X POST "<your-resource-endpoint>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview"
 -H "Content-Type: application/json"
 -H "Ocp-Apim-Subscription-Key: <YOUR-KEY>"
 --data-ascii "{
@@ -200,7 +200,7 @@ curl -i -X POST "<your-resource-name>/documentintelligence/documentModels/{model
 
 ```http
 HTTP/1.1 202 Accepted
-Operation-Location: https://<your-resource-name>.cognitiveservices.azure.com/documentintelligence/operations/{operation-id}?api-version=2024-02-29-preview
+Operation-Location: https://<your-resource-endpoint>.cognitiveservices.azure.com/documentintelligence/operations/{operation-id}?api-version=2024-02-29-preview
 ```
 
 ### Track copy operation progress

articles/ai-services/immersive-reader/overview.md

@@ -69,6 +69,10 @@ With Immersive Reader, you can break words into syllables to improve readability
 
 Immersive Reader is a standalone web application. When it's invoked, the Immersive Reader client library displays on top of your existing web application in an `iframe`. When your web application calls the Immersive Reader service, you specify the content to show the reader. The Immersive Reader client library handles the creation and styling of the `iframe` and communication with the Immersive Reader backend service. The Immersive Reader service processes the content for parts of speech, text to speech, translation, and more.
 
+## Data privacy for Immersive reader
+
+Immersive reader doesn't store any customer data.
+
 ## Next step
 
 The Immersive Reader client library is available in C#, JavaScript, Java (Android), Kotlin (Android), and Swift (iOS). Get started with:

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: 04/15/2024
+ms.date: 04/17/2024
 ms.custom: references_regions, build-2023, build-2023-dataai, refefences_regions
 manager: nitinme
 author: mrbullwinkle #ChrisHMSFT
@@ -92,6 +92,8 @@ You can also use the OpenAI text to speech voices via Azure AI Speech. To learn
 
 [!INCLUDE [Standard Models](../includes/model-matrix/standard-models.md)]
 
+This table does not include fine-tuning regional availability, consult the dedicated [fine-tuning section](#fine-tuning-models) for this information.
+
 ### Standard deployment model quota
 
 [!INCLUDE [Quota](../includes/model-matrix/quota.md)]
@@ -237,15 +239,15 @@ The following Embeddings models are available with [Azure Government](/azure/azu
 
 `babbage-002` and `davinci-002` are not trained to follow instructions. Querying these base models should only be done as a point of reference to a fine-tuned version to evaluate the progress of your training.
 
-`gpt-35-turbo-0613` - fine-tuning of this model is limited to a subset of regions, and is not available in every region the base model is available.  
+`gpt-35-turbo` - fine-tuning of this model is limited to a subset of regions, and is not available in every region the base model is available.  
 
 |  Model ID  | Fine-Tuning Regions | Max Request (tokens) | Training Data (up to) |
 |  --- | --- | :---: | :---: |
-| `babbage-002` | North Central US <br> Sweden Central | 16,384 | Sep 2021 |
-| `davinci-002` | North Central US <br> Sweden Central | 16,384 | Sep 2021 |
-| `gpt-35-turbo` (0613) | East US2 <br> North Central US <br> Sweden Central | 4,096 | Sep 2021 |
-| `gpt-35-turbo` (1106) | East US2 <br> North Central US <br> Sweden Central | Input: 16,385<br> Output: 4,096 |  Sep 2021|
-| `gpt-35-turbo` (0125)  | East US2 <br> North Central US <br> Sweden Central  | 16,385 | Sep 2021 |
+| `babbage-002` | North Central US <br> Sweden Central  <br> Switzerland West | 16,384 | Sep 2021 |
+| `davinci-002` | North Central US <br> Sweden Central  <br> Switzerland West | 16,384 | Sep 2021 |
+| `gpt-35-turbo` (0613) | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | 4,096 | Sep 2021 |
+| `gpt-35-turbo` (1106) | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | Input: 16,385<br> Output: 4,096 |  Sep 2021|
+| `gpt-35-turbo` (0125)  | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | 16,385 | Sep 2021 |
 
 ### Whisper models