Merge pull request #6934 from MicrosoftDocs/main

Auto Publish – main to live - 2025-09-04 17:10 UTC

Author: learn-build-service-prod[bot]

.gitignore

@@ -20,3 +20,5 @@ _repo.*/
 .github/prompts/*.md
 .github/prompts/*.zip
 .github/patterns/*.md
+
+articles/ai-foundry/includes/get-started-fdp.md

articles/ai-foundry/openai/concepts/provisioned-migration.md

@@ -7,9 +7,9 @@ ms.service: azure-ai-openai
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 06/30/2025
-author: aahill
-ms.author: aahi
+ms.date: 09/03/2025
+author: msakande
+ms.author: mopeakande
 recommendations: false
 ---
 

articles/ai-foundry/openai/concepts/provisioned-throughput.md

@@ -3,10 +3,10 @@ title: Provisioned throughput for Azure AI Foundry Models
 description: Learn about provisioned throughput and Azure AI Foundry.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 06/03/2025
+ms.date: 09/03/2025
 manager: nitinme
-author: aahill #ChrisHMSFT
-ms.author: aahi #chrhoder
+author: msakande #ChrisHMSFT
+ms.author: mopeakande #chrhoder
 ms.reviewer: shiyingfu
 reviewer: swingfu
 recommendations: false

articles/ai-foundry/openai/how-to/provisioned-get-started.md

@@ -6,9 +6,9 @@ manager: nitinme
 ms.service: azure-ai-openai
 ms.custom: openai
 ms.topic: how-to
-author: aahill
-ms.author: aahi
-ms.date: 06/30/2025
+author: msakande
+ms.author: mopeakande
+ms.date: 09/03/2025
 recommendations: false
 ---
 

articles/ai-foundry/openai/how-to/provisioned-throughput-onboarding.md

@@ -3,10 +3,10 @@ title:  Understanding costs associated with provisioned throughput units (PTU)
 description: Learn about provisioned throughput costs and billing in Azure AI Foundry. 
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 08/14/2025
+ms.date: 09/03/2025
 manager: nitinme
-author: aahill 
-ms.author: aahi 
+author: msakande 
+ms.author: mopeakande 
 recommendations: false
 ---
 

articles/ai-foundry/openai/how-to/spillover-traffic-management.md

@@ -1,11 +1,11 @@
 ---
 title: Manage traffic with spillover for Provisioned deployments
 description: Article outlining how to use the spillover feature to manage traffic bursts for Azure OpenAI in Azure AI Foundry Models provisioned deployments
-author: aahill # GitHub alias
-ms.author: aahi
+author: msakande
+ms.author: mopeakande
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 08/27/2025
+ms.date: 09/03/2025
 ---
 
 # Manage traffic with spillover for provisioned deployments

articles/ai-services/document-intelligence/quickstarts/includes/rest-api.md

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: include
-ms.date: 07/17/2025
+ms.date: 07/21/2025
 ms.author: lajanuar
 ---
 
@@ -30,7 +30,7 @@ In this quickstart, learn to use the Document Intelligence REST API to analyze a
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
 
-* cURL command-line tool installed.
+* The `cURL` command line tool installed.
 
 * **PowerShell version 7.*+** (or a similar command-line application.):
   * [Windows](/powershell/scripting/install/installing-powershell-on-windows)

articles/ai-services/language-service/conversational-language-understanding/how-to/use-containers.md

@@ -39,9 +39,9 @@ The following table describes the minimum and recommended specifications for the
 
 We recommended that you have a CPU with AVX-512 instruction set, for the best experience (performance and accuracy).
 
-|     | Minimum host specs     | Recommended host specs |
+|  Processor   | Minimum host specs     | Recommended host specs |
 |---------------------|------------------------|------------------------|
-| **CLU**     | `1 core`, `2-GB memory`     | `4 cores`, `8-GB memory`    |
+| **CPU**     | 1-core, 2-GB memory     | 4-cores, 8-GB memory    |
 
 CPU core and memory correspond to the `--cpus` and `--memory` settings, which are used as part of the `docker run` command.
 
@@ -54,7 +54,7 @@ Before you proceed with running the docker image, you need to export your own tr
 |**{API_KEY}** |The key for your Language resource. You can find it on your resource's **Key and endpoint** page, on the Azure portal.|xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx|
 |**{ENDPOINT_URI}**|The endpoint for accessing the Conversational Language Understanding API. You can find it on your resource's **Key and endpoint** page, on the Azure portal.|`https://<your-custom-subdomain>.cognitiveservices.azure.com`|
 |**{PROJECT_NAME}**|The name of the project containing the model that you want to export. You can find it on your projects tab in the Language Studio portal.|myProject|
-|**{TRAINED_MODEL_NAME}** |The name of the trained model you want to export. You can find your trained models on your model evaluation tab under your project in the Language Studio portal|myTrainedModel
+|**{TRAINED_MODEL_NAME}** |The name of the trained model you want to export. You can find your trained models on your model evaluation tab under your project in the Language Studio portal|myTrainedModel|
 |**{EXPORTED_MODEL_NAME}** |The name to assign for the new exported model created.|myExportedModel |
 
 ```bash
@@ -74,8 +74,8 @@ The CLU container image can be found on the `mcr.microsoft.com` container regist
 
 The latest CLU container is available in several languages. To download the container for the English container, use the following command:
 
-```
-docker pull mcr.microsoft.com/azure-cognitive-services/language/clu:latest
+```bash
+  docker pull mcr.microsoft.com/azure-cognitive-services/language/clu:latest
 ```
 
 [!INCLUDE [Tip for using docker list](../../../includes/cognitive-services-containers-docker-list-tip.md)]
@@ -94,16 +94,18 @@ After the exported model is created, users have to run the container to download
 | **{PROJECT_NAME}**| Name of the project that the exported model belongs to  | myProject  |
 | **{EXPORTED_MODEL_NAME}**   | Exported model to be downloaded | myExportedModel   |
 
-```bash
-docker run --rm -it -p {LOCAL_CLU_PORT}:80 \
-mcr.microsoft.com/azure-cognitive-services/language/clu:{IMAGE_TAG} \ 
--v {LOCAL_MODEL_DIRECTORY}:/DeploymentPackage \
-Billing={ENDPOINT_URI} \ 
-ApiKey={API_KEY} \
-downloadmodel \
-projectName={PROJECT_NAME} \
-exportedModelName={EXPORTED_MODEL_NAME}
-```
+
+  
+  ```bash
+    docker run --rm -it -p {LOCAL_CLU_PORT}:80 \
+    mcr.microsoft.com/azure-cognitive-services/language/clu:{IMAGE_TAG} \ 
+    -v {LOCAL_MODEL_DIRECTORY}:/DeploymentPackage \
+    Billing={ENDPOINT_URI} \ 
+    ApiKey={API_KEY} \
+    downloadmodel \
+    projectName={PROJECT_NAME} \
+    exportedModelName={EXPORTED_MODEL_NAME}
+  ```
 
 DO NOT alter the downloaded files. Even altering the name or folder structure can affect the integrity of the container and might break it.
 

articles/ai-services/language-service/personally-identifiable-information/how-to/adapt-to-domain-pii.md

@@ -42,7 +42,7 @@ Once you test the service with your own data, you can use `entitySynonyms` to:
 ## Usage guidelines
 
 1. Synonyms must be restricted to phrases that directly refer to the type, and preserve semantic correctness. For example, for the entity type `InternationalBankAccountNumber`, a valid synonym could be "Financial Account Number" or *FAN*. But, the word *deposit* though may be associated with type, as it doesn't directly have a meaning of a bank account number and therefore shouldn't be used.
-1. Synonyms should be country agnostic. For example, *German passport* wouldn't be helpful to include.
+1. Synonyms should be country/region agnostic. For example, *German passport* wouldn't be helpful to include.
 1. Synonyms can't be reused for more than one entity type.
 1. This synonym recognition feature only accepts a subset of entity types supported by the service. The supported entity types and example synonyms include:
 

articles/ai-services/translator/containers/transliterate-text-parameters.md

@@ -66,7 +66,7 @@ A successful response is a JSON array with one result for each element in the in
 ### Sample request
 
 ```bash
-curl -X POST "http://localhost:5000/transliterate?api-version=3.0&language=ja&fromScript=Jpan&toScript=Latn"
+curl -X POST "http://localhost:5000/transliterate?api-version=3.0&language=ja&fromScript=Jpan&toScript=Latin"
 ```
 
 ### Sample request body
@@ -92,11 +92,11 @@ The following limitations apply:
 [
   {
     "text": "Kon'nichiwa​",
-    "script": "Latn"
+    "script": "Latin"
   },
   {
     "text": "sayonara",
-    "script": "Latn"
+    "script": "Latin"
   }
 ]
 ```
@@ -111,7 +111,7 @@ The following limitations apply:
 
 ```bash
 
-  curl -X POST "http://localhost:5000/transliterate?api-version=3.0&language=ja&fromScript=Jpan&toScript=Latn" -H "Content-Type: application/json" -d "[{'Text':'こんにちは'},{'Text':'さようなら'}]"
+  curl -X POST "http://localhost:5000/transliterate?api-version=3.0&language=ja&fromScript=Jpan&toScript=Latin" -H "Content-Type: application/json" -d "[{'Text':'こんにちは'},{'Text':'さようなら'}]"
 
 ```