resolve conflict

Author: b-ahibbard

.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/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/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
 

articles/ai-services/openai/how-to/content-filters.md

@@ -6,7 +6,7 @@ description: Learn how to use content filters (preview) with Azure OpenAI Servic
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 03/29/2024
+ms.date: 04/16/2024
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -15,7 +15,7 @@ recommendations: false
 # How to configure content filters with Azure OpenAI Service
 
 > [!NOTE]
-> All customers have the ability to modify the content filters to be stricter (for example, to filter content at lower severity levels than the default). Approval is required for turning the content filters partially or fully off. Managed customers only may apply for full content filtering control via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR).
+> All customers have the ability to modify the content filters and configure the severity thresholds (low, medium, high). Approval is required for turning the content filters partially or fully off. Managed customers only may apply for full content filtering control via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR).
 
 The content filtering system integrated into Azure OpenAI Service runs alongside the core models and uses an ensemble of multi-class classification models to detect four categories of harmful content (violence, hate, sexual, and self-harm) at four severity levels respectively (safe, low, medium, and high), and optional binary classifiers for detecting jailbreak risk, existing text, and code in public repositories. The default content filtering configuration is set to filter at the medium severity threshold for all four content harms categories for both prompts and completions. That means that content that is detected at severity level medium or high is filtered, while content detected at severity level low or safe is not filtered by the content filters. Learn more about content categories, severity levels, and the behavior of the content filtering system [here](../concepts/content-filter.md). Jailbreak risk detection and protected text and code models are optional and off by default. For jailbreak and protected material text and code models, the configurability feature allows all customers to turn the models on and off. The models are by default off and can be turned on per your scenario. Some models are required to be on for certain scenarios to retain coverage under the [Customer Copyright Commitment](/legal/cognitive-services/openai/customer-copyright-commitment?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext).
 

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

@@ -75,7 +75,17 @@ If you would like a step-by-step walk-through of fine-tuning a `gpt-35-turbo-061
 {"messages": [{"role": "system", "content": "You are an Xbox customer support agent whose primary goal is to help users with issues they are experiencing with their Xbox devices. You are friendly and concise. You only provide factual answers to queries, and do not provide answers that are not related to Xbox."}, {"role": "user", "content": "I'm having trouble connecting my Xbox to the Wi-Fi."}, {"role": "assistant", "content": "No worries, let's go through the network settings on your Xbox. Can you please tell me what happens when you try to connect it to the Wi-Fi?"}]}
 ```
 
-In addition to the JSONL format, training and validation data files must be encoded in UTF-8 and include a byte-order mark (BOM). The file must be less than 100 MB in size.
+### Multi-turn chat file format
+
+Multiple turns of a conversation in a single line of your jsonl training file is also supported. To skip fine-tuning on specific assistant messages add the optional `weight` key value pair. Currently `weight` can be set to 0 or 1.  
+
+```json
+{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What's the capital of France?"}, {"role": "assistant", "content": "Paris", "weight": 0}, {"role": "user", "content": "Can you be more sarcastic?"}, {"role": "assistant", "content": "Paris, as if everyone doesn't know that already.", "weight": 1}]}
+{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "Who wrote 'Romeo and Juliet'?"}, {"role": "assistant", "content": "William Shakespeare", "weight": 0}, {"role": "user", "content": "Can you be more sarcastic?"}, {"role": "assistant", "content": "Oh, just some guy named William Shakespeare. Ever heard of him?", "weight": 1}]}
+{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "How far is the Moon from Earth?"}, {"role": "assistant", "content": "384,400 kilometers", "weight": 0}, {"role": "user", "content": "Can you be more sarcastic?"}, {"role": "assistant", "content": "Around 384,400 kilometers. Give or take a few, like that really matters.", "weight": 1}]}
+```
+
+In addition to the JSONL format, training and validation data files must be encoded in UTF-8 and include a byte-order mark (BOM). The file must be less than 512 MB in size.
 
 ### Create your training and validation datasets
 
@@ -93,7 +103,7 @@ The training and validation data you use **must** be formatted as a JSON Lines (
 {"prompt": "<prompt text>", "completion": "<ideal generated text>"}
 ```
 
-In addition to the JSONL format, training and validation data files must be encoded in UTF-8 and include a byte-order mark (BOM). The file must be less than 100 MB in size. 
+In addition to the JSONL format, training and validation data files must be encoded in UTF-8 and include a byte-order mark (BOM). The file must be less than 512 MB in size.
 
 ### Create your training and validation datasets
 
@@ -263,7 +273,7 @@ print(response)
 
 ---
 
-You can also pass additional optional parameters like hyperparameters to take greater control of the fine-tuning process. For initial training we recommend using the automatic defaults that are present without specifying these parameters.
+You can also pass additional optional parameters like hyperparameters to take greater control of the fine-tuning process. For initial training we recommend using the automatic defaults that are present without specifying these parameters. 
 
 The current supported hyperparameters for fine-tuning are: