Merge branch 'main' into release-machine-config-toc-reorganization

Author: v-alje

.openpublishing.redirection.json

@@ -5707,7 +5707,77 @@
         },
         {
             "source_path_from_root": "/articles/industrial-iot/overview-what-is-industrial-iot-platform.md",
-            "redirect_url": "/azure/industrial-iot/overview-what-is-industrial-iot",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/index.yml",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/industrial-iot-platform-versions.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/overview-what-is-industrial-iot.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/overview-what-is-opc-publisher.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/reference-command-line-arguments.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/reference-opc-publisher-telemetry-format.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/tutorial-configure-industrial-iot-components.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/tutorial-data-explorer-import.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/tutorial-deploy-industrial-iot-platform.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/tutorial-industrial-iot-azure-data-explorer.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/tutorial-publisher-configure-opc-publisher.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/tutorial-publisher-deploy-opc-publisher-standalone.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/tutorial-publisher-performance-memory-tuning-opc-publisher.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/industrial-iot/tutorial-visualize-data-time-series-insights.md",
+            "redirect_url": "https://github.com/Azure/Industrial-IoT/blob/main/readme.md",
             "redirect_document_id": false
         },
         {
@@ -10798,6 +10868,13 @@
             "source_path_from_root": "/articles/azure-health-insights/response-info.md",
             "redirect_url": "/azure/azure-health-insights/overview",
             "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/networking/disaster-recovery-dns-traffic-manager.md",
+            "redirect_url": "/azure/reliability/reliability-traffic-manager",
+            "redirect_document_id": false
         }
+
+        
     ]
 }

articles/active-directory-b2c/custom-policies-series-validate-user-input.md

@@ -379,7 +379,7 @@ Follow the steps in [Upload custom policy file](custom-policies-series-hello-wor
 
 ## Step 7 - Validate user input by using validation technical profiles
 
-The validation techniques we've used in step 1, step 2 and step 3 aren't applicable for all scenarios. If your business rules are complex to be defined at claim declaration level, you can configure a [Validation Technical](validation-technical-profile.md), and then call it from a [Self-Asserted Technical Profile](self-asserted-technical-profile.md).
+The validation techniques we've used in step 1, step 2 and step 3 aren't applicable for all scenarios. If your business rules are too complex to be defined at claim declaration level, you can configure a [Validation Technical](validation-technical-profile.md), and then call it from a [Self-Asserted Technical Profile](self-asserted-technical-profile.md).
 
 > [!NOTE] 
 > Only self-asserted technical profiles can use validation technical profiles. Learn more about [validation technical profile](validation-technical-profile.md) 

articles/ai-services/document-intelligence/containers/install-run.md

@@ -18,25 +18,29 @@ ms.author: lajanuar
 <!-- markdownlint-disable MD024 -->
 <!-- markdownlint-disable MD051 -->
 
-:::moniker range="doc-intel-2.1.0 || doc-intel-3.1.0||doc-intel-4.0.0"
+:::moniker range="doc-intel-2.1.0 || doc-intel-4.0.0"
 
-Support for containers is currently available with Document Intelligence version `2022-08-31 (GA)` only:
+Support for containers is currently available with Document Intelligence version `2022-08-31 (GA)` for all models and `2023-07-31 (GA)` for Read and Layout only:
 
 * [REST API `2022-08-31 (GA)`](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-2022-08-31/operations/AnalyzeDocument)
+* [REST API `2023-07-31 (GA)`](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-2023-07-31/operations/AnalyzeDocument)
 * [SDKs targeting `REST API 2022-08-31 (GA)`](../sdk-overview-v3-0.md)
+* [SDKs targeting `REST API 2023-07-31 (GA)`](../sdk-overview-v3-1.md)
 
 ✔️ See [**Install and run Document Intelligence v3.0 containers**](?view=doc-intel-3.0.0&preserve-view=true) for supported container documentation.
 
 :::moniker-end
 
-:::moniker range="doc-intel-3.0.0"
+:::moniker range="doc-intel-3.0.0 || doc-intel-3.1.0"
 
-**This content applies to:** ![checkmark](../media/yes-icon.png) **v3.0 (GA)**
+**This content applies to:** ![checkmark](../media/yes-icon.png) **v3.0 (GA)** ![checkmark](../media/yes-icon.png) **v3.1 (GA)**
 
 Azure AI Document Intelligence is an Azure AI service that lets you build automated data processing software using machine-learning technology. Document Intelligence enables you to identify and extract text, key/value pairs, selection marks, table data, and more from your documents. The results are delivered as structured data that ../includes the relationships in the original file.
 
 In this article you learn how to download, install, and run Document Intelligence containers. Containers enable you to run the Document Intelligence service in your own environment. Containers are great for specific security and data governance requirements.
 
+* **Read**, and **Layout**  models are supported by Document Intelligence v3.1 containers.
+
 * **Read**, **Layout**, **General Document**, **ID Document**,  **Receipt**, **Invoice**, **Business Card**, and **Custom** models are supported by Document Intelligence v3.0 containers.
 
 * **Business Card** model is currently only supported in the [v2.1 containers](install-run.md?view=doc-intel-2.1.0&preserve-view=true).

articles/ai-services/document-intelligence/faq.yml

@@ -520,6 +520,14 @@ sections:
 
           Yes. The labeling experience from Studio is open sourced in the [Toolkit repo](https://github.com/microsoft/Form-Recognizer-Toolkit)
 
+      - question: |
+          Why am I receiving 'Form Recognizer Not Found' error when opening my custom project?
+        answer: |
+          Your Document Intelligence resource bound to this custom project may have been deleted or moved to another resource group.  There are two ways to resolve this issue:
+          
+            - Re-create the Document Intelligence resource under the same subscription/resource group with the same name.
+          
+            - Re-create a custom project with the migrated Document Intelligence resource and specify the very same storage account.
 
   - name: Containers
     questions:

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

@@ -88,7 +88,7 @@ See [model versions](../concepts/model-versions.md) to learn about how Azure Ope
 > Version `0314` of `gpt-4` and `gpt-4-32k` will be retired no earlier than July 5, 2024.  Version `0613` of `gpt-4` and `gpt-4-32k` will be retired no earlier than September 30, 2024.  See [model updates](../how-to/working-with-models.md#model-updates) for model upgrade behavior.
 
 
-GPT-4 version 0125-preview is an updated version of the GPT-4 Turbo preview previously released as version 1106-preview.  GPT-4 versio 0125-preview completes tasks such as code generation more completely compared to gpt-4-1106-preview.  Because of this, depending on the task, customers may find that GPT-4-0125-preview generates more output compared to the gpt-4-1106-preview.  We recommend customers compare the outputs of the new model.  GPT-4-0125-preview also addresses bugs in gpt-4-1106-preview with UTF-8 handling for non-English languages. 
+GPT-4 version 0125-preview is an updated version of the GPT-4 Turbo preview previously released as version 1106-preview.  GPT-4 version 0125-preview completes tasks such as code generation more completely compared to gpt-4-1106-preview.  Because of this, depending on the task, customers may find that GPT-4-0125-preview generates more output compared to the gpt-4-1106-preview.  We recommend customers compare the outputs of the new model.  GPT-4-0125-preview also addresses bugs in gpt-4-1106-preview with UTF-8 handling for non-English languages. 
 
 > [!IMPORTANT]
 >
@@ -144,7 +144,7 @@ GPT-3.5 Turbo version 0301 is the first version of the model released.  Version
 See [model versions](../concepts/model-versions.md) to learn about how Azure OpenAI Service handles model version upgrades, and [working with models](../how-to/working-with-models.md) to learn how to view and configure the model version settings of your GPT-3.5 Turbo deployments.
 
 > [!NOTE]
-> Version `0613` of `gpt-35-turbo` and `gpt-35-turbo-16k` will be retired on June 13, 2024. Version `0301` of `gpt-35-turbo` will be retired on July 5, 2024.  See [model updates](../how-to/working-with-models.md#model-updates) for model upgrade behavior.
+> Version `0613` of `gpt-35-turbo` and `gpt-35-turbo-16k` will be retired no earlier than June 13, 2024. Version `0301` of `gpt-35-turbo` will be retired no earlier than July 5, 2024.  See [model updates](../how-to/working-with-models.md#model-updates) for model upgrade behavior.
 
 ### GPT-3.5-Turbo model availability
 

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

@@ -5,7 +5,7 @@ description: Learn about performance and latency with Azure OpenAI
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 11/21/2023
+ms.date: 02/07/2024
 author: mrbullwinkle 
 ms.author: mbullwin
 recommendations: false
@@ -58,17 +58,17 @@ Latency varies based on what model you're using. For an identical request, expec
 
 When you send a completion request to the Azure OpenAI endpoint, your input text is converted to tokens that are then sent to your deployed model. The model receives the input tokens and then begins generating a response. It's an iterative sequential process, one token at a time. Another way to think of it is like a for loop with `n tokens = n iterations`. For most models, generating the response is the slowest step in the process.  
 
-At the time of the request, the requested generation size (max_tokens parameter) is used as an initial estimate of the generation size. The compute-time for generating the full size is reserved the model as the request is processed. Once the generation is completed, the remaining quota is released. Ways to reduce the number of tokens:
-o	Set the `max_token` parameter on each call as small as possible.
-o	Include stop sequences to prevent generating extra content. 
-o	Generate fewer responses: The best_of & n parameters can greatly increase latency because they  generate multiple outputs. For the fastest response, either don't specify these values or set them to 1.
+At the time of the request, the requested generation size (max_tokens parameter) is used as an initial estimate of the generation size. The compute-time for generating the full size is reserved by the model as the request is processed. Once the generation is completed, the remaining quota is released. Ways to reduce the number of tokens:
+- Set the `max_token` parameter on each call as small as possible.
+- Include stop sequences to prevent generating extra content.
+- Generate fewer responses: The best_of & n parameters can greatly increase latency because they generate multiple outputs. For the fastest response, either don't specify these values or set them to 1.
 
 In summary, reducing the number of tokens generated per request reduces the latency of each request.
 
 ### Streaming
 Setting `stream: true` in a request makes the service return tokens as soon as they're available, instead of waiting for the full sequence of tokens to be generated. It doesn't change the time to get all the tokens, but it reduces the time for first response. This approach provides a better user experience since end-users can read the response as it is generated. 
 
-Streaming is also valuable with large calls that take a long time to process. Many clients and intermediary layers have timeouts on individual calls. Long generation calls might be canceled due to client-side time outs. By streaming the data back, you can ensure incremental data is received. 
+Streaming is also valuable with large calls that take a long time to process. Many clients and intermediary layers have timeouts on individual calls. Long generation calls might be canceled due to client-side time outs. By streaming the data back, you can ensure incremental data is received.
 
 
 

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

@@ -18,6 +18,12 @@ recommendations: false
 
 ## February 2024
 
+### GPT-4-0125-preview model available
+
+The `gpt-4` model version `0125-preview` is now available on Azure OpenAI Service in the East US, North Central US, and South Central US regions.  Customers with deployments of `gpt-4` version `1106-preview` will be automatically upgraded to `0125-preview` in the coming weeks.  
+
+For information on model regional availability and upgrades refer to the [models page](./concepts/models.md).
+
 ### Assistants API public preview
 
 Azure OpenAI now supports the API that powers OpenAI's GPTs. Azure OpenAI Assistants (Preview) allows you to create AI assistants tailored to your needs through custom instructions and advanced tools like code interpreter, and custom functions. To learn more, see:

articles/ai-services/security-controls-policy.md

@@ -1,7 +1,7 @@
 ---
 title: Azure Policy Regulatory Compliance controls for Azure AI services
 description: Lists Azure Policy Regulatory Compliance controls available for Azure AI services. These built-in policy definitions provide common approaches to managing the compliance of your Azure resources.
-ms.date: 01/22/2024
+ms.date: 02/06/2024
 ms.topic: sample
 author: PatrickFarley
 ms.author: pafarley

articles/ai-services/speech-service/includes/how-to/professional-voice/create-consent/rest.md

@@ -11,7 +11,7 @@
 
 With the professional voice feature, it's required that every voice be created with explicit consent from the user. A recorded statement from the user is required acknowledging that the customer (Azure AI Speech resource owner) will create and use their voice.
 
-To add voice talent consent to the professional voice project, you get the prerecorded consent audio file from a publicly accessible URL (`Consents_Create`) or upload the audio file (`Consents_Post`). In this article, you add consent from a URL. 
+To add voice talent consent to the professional voice project, you get the prerecorded consent audio file from a publicly accessible URL ([Consents_Create](/rest/api/speechapi/consents/create)) or upload the audio file ([Consents_Post](/rest/api/speechapi/consents/post)). In this article, you add consent from a URL. 
 
 ## Consent statement
 
@@ -25,15 +25,15 @@ You can get the consent statement text for each locale from the text to speech G
 
 ## Add consent from a URL
 
-To add consent to a professional voice project from the URL of an audio file, use the `Consents_Create` operation of the custom voice API. Construct the request body according to the following instructions:
+To add consent to a professional voice project from the URL of an audio file, use the [Consents_Create](/rest/api/speechapi/consents/create) operation of the custom voice API. Construct the request body according to the following instructions:
 
 - Set the required `projectId` property. See [create a project](../../../../professional-voice-create-project.md).
 - Set the required `voiceTalentName` property. The voice talent name can't be changed later.
 - Set the required `companyName` property. The company name can't be changed later.
 - Set the required `audioUrl` property. The URL of the voice talent consent audio file. Use a URI with the [shared access signatures (SAS)](/azure/storage/common/storage-sas-overview) token.
 - Set the required `locale` property. This should be the locale of the consent. The locale can't be changed later. You can find the text to speech locale list [here](/azure/ai-services/speech-service/language-support?tabs=tts).
 
-Make an HTTP PUT request using the URI as shown in the following `Consents_Create` example. 
+Make an HTTP PUT request using the URI as shown in the following [Consents_Create](/rest/api/speechapi/consents/create) example. 
 - Replace `YourResourceKey` with your Speech resource key.
 - Replace `YourResourceRegion` with your Speech resource region.
 - Replace `JessicaConsentId` with a consent ID of your choice. The case sensitive ID will be used in the consent's URI and can't be changed later. 
@@ -65,7 +65,7 @@ You should receive a response body in the following format:
 }
 ```
 
-The response header contains the `Operation-Location` property. Use this URI to get details about the `Consents_Create` operation. Here's an example of the response header:
+The response header contains the `Operation-Location` property. Use this URI to get details about the [Consents_Create](/rest/api/speechapi/consents/create) operation. Here's an example of the response header:
 
 ```HTTP 201
 Operation-Location: https://eastus.api.cognitive.microsoft.com/customvoice/operations/070f7986-ef17-41d0-ba2b-907f0f28e314?api-version=2023-12-01-preview