Merge branch 'main' of https://github.com/microsoftdocs/azure-ai-docs-pr into prompt2

Author: v-thepet

.github/policies/disallow-edits.yml

@@ -12,10 +12,10 @@ configuration:
               action: Opened
           - or:              
               - filesMatchPattern:
-                  pattern: articles/index.yml
+                  matchAny: true
+                  pattern: articles/index.*
         then:
           - addReply:
               reply: >-
                 @${issueAuthor} - You tried to add an index file to this repository; this is not permitted so your pull request will be closed automatically. 
           - closePullRequest
-          

articles/ai-services/.openpublishing.redirection.ai-services.json

@@ -484,6 +484,11 @@
       "source_path_from_root": "/articles/ai-services/language-service/summarization/custom/quickstart.md",
       "redirect_url": "/azure/ai-services//language-service/summarization/overview",
       "redirect_document_id": false
-    }
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/speech-service/video-translation-studio.md",
+      "redirect_url": "/azure/ai-services/speech-service/video-translation-get-started",
+      "redirect_document_id": true
+   }
   ]
-}
+}

articles/ai-services/encryption/cognitive-services-encryption-keys-portal.md

@@ -27,20 +27,6 @@ Azure AI is built on top of multiple Azure services. While the data is stored se
         * unwrap key
         * get
 
-        For example, the managed identity for Azure Cosmos DB would need to have those permissions to the key vault.
-
-## How metadata is stored
-
-The following services are used by Azure AI to store metadata for your Azure AI resource and projects:
-
-|Service|What it's used for|Example|
-|-----|-----|-----|
-|Azure Cosmos DB|Stores metadata for your Azure AI projects and tools|Flow creation timestamps, deployment tags, evaluation metrics|
-|Azure AI Search|Stores indices that are used to help query your AI studio content.|An index based off your model deployment names|
-|Azure Storage Account|Stores artifacts created by Azure AI projects and tools|Fine-tuned models|
-
-All of the above services are encrypted using the same key at the time that you create your Azure AI resource for the first time, and are set up in a managed resource group in your subscription once for every Azure AI resource and set of projects associated with it. Your Azure AI resource and projects read and write data using managed identity. Managed identities are granted access to the resources using a role assignment (Azure role-based access control) on the data resources. The encryption key you provide is used to encrypt data that is stored on Microsoft-managed resources. It's also used to create indices for Azure AI Search, which are created at runtime.
-
 ## Customer-managed keys
 
 When you don't use a customer-managed key, Microsoft creates and manages these resources in a Microsoft owned Azure subscription and uses a Microsoft-managed key to encrypt the data. 
@@ -53,14 +39,13 @@ When you use a customer-managed key, these resources are _in your Azure subscrip
 These Microsoft-managed resources are located in a new Azure resource group is created in your subscription. This group is in addition to the resource group for your project. This resource group contains the Microsoft-managed resources that your key is used with. The resource group is named using the formula of `<Azure AI resource group name><GUID>`. It isn't possible to change the naming of the resources in this managed resource group.
 
 > [!TIP]
-> * The [Request Units](/azure/cosmos-db/request-units) for the Azure Cosmos DB automatically scale as needed.
 > * If your AI resource uses a private endpoint, this resource group will also contain a Microsoft-managed Azure Virtual Network. This VNet is used to secure communications between the managed services and the project. You cannot provide your own VNet for use with the Microsoft-managed resources. You also cannot modify the virtual network. For example, you cannot change the IP address range that it uses.
 
 > [!IMPORTANT]
 > If your subscription does not have enough quota for these services, a failure will occur.
 
 > [!WARNING]
-> Don't delete the managed resource group that contains this Azure Cosmos DB instance, or any of the resources automatically created in this group. If you need to delete the resource group or Microsoft-managed services in it, you must delete the Azure AI resources that uses it. The resource group resources are deleted when the associated AI resource is deleted.
+> Don't delete the managed resource group any of the resources automatically created in this group. If you need to delete the resource group or Microsoft-managed services in it, you must delete the Azure AI resources that uses it. The resource group resources are deleted when the associated AI resource is deleted.
 
 The process to enable Customer-Managed Keys with Azure Key Vault for Azure AI services varies by product. Use these links for service-specific instructions:
 

articles/ai-services/language-service/personally-identifiable-information/how-to/use-containers.md

@@ -1,7 +1,7 @@
 ---
-title: Use personally identifiable information Docker containers on-premises
+title: Use personally identifiable information (PII) detection Docker containers on-premises
 titleSuffix: Azure AI services
-description: Use Docker containers for the Personally Identifiable Information API to determine the language of written text, on-premises.
+description: Use Docker containers for the Personally Identifiable Information (PII) detection API to determine the language of written text, on-premises.
 #services: cognitive-services
 author: jboback
 manager: nitinme
@@ -16,7 +16,7 @@ keywords: on-premises, Docker, container
 # Install and run Personally Identifiable Information (PII) Detection containers
 
 > [!NOTE]
-> The data limits for the PII container are 5120 characters and 10 documents max.
+> The data limits in a single synchronous API call for the PII container are 5120 characters per document and up to 10 documents per call.
 
 Containers enable you to host the PII detection API on your own infrastructure. If you have security or data governance requirements that can't be fulfilled by calling PII detection remotely, then containers might be a good option.
 

articles/ai-services/openai/concepts/model-retirements.md

@@ -102,7 +102,8 @@ These models are currently available for use in Azure OpenAI Service.
 | `gpt-4` | 1106-preview | To be upgraded to `gpt-4` version: `turbo-2024-04-09`, starting no sooner than January 27, 2025 **<sup>1</sup>** | `gpt-4o`|
 | `gpt-4` | 0125-preview |To be upgraded to `gpt-4` version: `turbo-2024-04-09`, starting no sooner than January 27, 2025 **<sup>1</sup>**  | `gpt-4o` |
 | `gpt-4` | vision-preview | To be upgraded to `gpt-4` version: `turbo-2024-04-09`, starting no sooner than January 27, 2025  **<sup>1</sup>** | `gpt-4o`|
-| `gpt-4o` | 2024-05-13 | No earlier than March 20, 2025 <br><br>Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `2024-08-06`, starting on December 5, 2024. | |
+| `gpt-4o` | 2024-05-13 | No earlier than May 20, 2025 <br><br>Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `2024-08-06`, starting on December 5, 2024. | |
+| `gpt-4o-mini` | 2024-07-18 | No earlier than July 18, 2025  | |
 | `gpt-3.5-turbo-instruct` | 0914 | No earlier than Sep 14, 2025 |  |
 | `text-embedding-ada-002` | 2 | No earlier than April 3, 2025 | `text-embedding-3-small` or `text-embedding-3-large` |
 | `text-embedding-ada-002` | 1 | No earlier than April 3, 2025 | `text-embedding-3-small` or `text-embedding-3-large` |
@@ -119,8 +120,7 @@ These models are currently available for use in Azure OpenAI Service.
 | Model | Current default version | New default version | Default upgrade date |
 |---|---|---|---|
 | `gpt-35-turbo` | 0301 | 0125 | Deployments of versions `0301`, `0613`, and `1106` set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on November 13, 2024.|
-
-
+|  `gpt-4o` | 2024-05-13 | 2024-08-06 | Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `2024-08-06`, starting on December 5, 2024. |
 
 ## Deprecated models
 

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

@@ -6,7 +6,7 @@ manager: nitinme
 ms.service: azure-ai-openai
 ms.custom: 
 ms.topic: how-to
-ms.date: 10/02/2024
+ms.date: 10/11/2024
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -65,9 +65,9 @@ The following models support global batch:
 
 Refer to the [models page](../concepts/models.md) for the most up-to-date information on regions/models where global batch is currently supported.
 
-### API Versions
+### API support
 
-- `2024-07-01-preview`
+API support was first added with `2024-07-01-preview`. 
 
 ### Not supported
 
@@ -224,8 +224,7 @@ When a job failure occurs, you'll find details about the failure in the `errors`
 
 ### Known issues
 
-- Resources deployed with Azure CLI won't work out-of-box with Azure OpenAI global batch. This is due to an issue where resources deployed using this method have endpoint subdomains that don't follow the `https://your-resource-name.openai.azure.com` pattern. A workaround for this issue is to deploy a new Azure OpenAI resource using one of the other common deployment methods which will properly handle the subdomain setup as part of the deployment process. 
-
+- Resources deployed with Azure CLI won't work out-of-box with Azure OpenAI global batch. This is due to an issue where resources deployed using this method have endpoint subdomains that don't follow the `https://your-resource-name.openai.azure.com` pattern. A workaround for this issue is to deploy a new Azure OpenAI resource using one of the other common deployment methods which will properly handle the subdomain setup as part of the deployment process.
 
 ## See also
 

articles/ai-services/openai/includes/batch/batch-python.md

@@ -104,34 +104,11 @@ file_id = file.id
   "filename": "test.jsonl",
   "object": "file",
   "purpose": "batch",
-  "status": "pending",
+  "status": null,
   "status_details": null
 }
 ```
 
-## Track file upload status
-
-Depending on the size of your upload file it might take some time before it's fully uploaded and processed. To check on your file upload status run:
-
-```python
-# Wait until the uploaded file is in processed state
-import time
-import datetime 
-
-status = "pending"
-while status != "processed":
-    time.sleep(15)
-    file_response = client.files.retrieve(file_id)
-    status = file_response.status
-    print(f"{datetime.datetime.now()} File Id: {file_id}, Status: {status}")
-```
-
-**Output:**
-
-```output
-2024-07-31 21:42:53.663655 File Id: file-9f3a81d899b4442f98b640e4bc3535dd, Status: processed
-```
-
 ## Create batch job
 
 Once your file has uploaded successfully by reaching a status of `processed` you can submit the file for batch processing.

articles/ai-services/openai/quotas-limits.md

@@ -10,7 +10,7 @@ ms.custom:
   - ignite-2023
   - references_regions
 ms.topic: conceptual
-ms.date: 10/10/2024
+ms.date: 10/11/2024
 ms.author: mbullwin
 ---
 
@@ -62,6 +62,17 @@ The following sections provide you with a quick guide to the default quotas and
 
 ## o1-preview & o1-mini rate limits
 
+> [!IMPORTANT]
+> The ratio of RPM/TPM for quota with o1-series models works differently than older chat completions models:
+>
+> - **Older chat models:** 1 unit of capacity = 6 RPM and 1,000 TPM.
+> - **o1-preview:** 1 unit of capacity = 1 RPM and 6,000 TPM.
+> - **o1-mini:** 1 unit of capacity = 1 RPM per 10,000 TPM.
+>
+> This is particularly important for programmatic model deployment as this change in RPM/TPM ratio can result in accidental under allocation of quota if one is still assuming the 1:1000 ratio followed by older chat completion models.
+>
+> There is a known issue with the [quota/usages API](/rest/api/aiservices/accountmanagement/usages/list?view=rest-aiservices-accountmanagement-2024-06-01-preview&tabs=HTTP&preserve-view=true) where it assumes the old ratio applies to the new o1-series models. The API returns the correct base capacity number, but does not apply the correct ratio for the accurate calculation of TPM.
+
 ### o1-preview & o1-mini global standard
 
 | Model|Tier| Quota Limit in tokens per minute (TPM) | Requests per minute |

articles/ai-services/speech-service/high-definition-voices.md

@@ -39,7 +39,7 @@ Here's a comparison of features between Azure AI Speech HD voices, Azure OpenAI
 
 | Feature | Azure AI Speech HD voices  | Azure OpenAI HD voices | Azure AI Speech voices (not HD) |
 |---------|---------------|------------------------|------------------------|
-| **Region** | North Central US, Sweden Central | North Central US, Sweden Central | Available in dozens of regions. See the [region list](regions.md#speech-service).|
+| **Region** | East US, Southeast Asia, West Europe | North Central US, Sweden Central | Available in dozens of regions. See the [region list](regions.md#speech-service).|
 | **Number of voices** | 12 | 6 | More than 500 |
 | **Multilingual**  | No (perform on primary language only) | Yes  | Yes (applicable only to multilingual voices)  |
 | **SSML support** | Support for [a subset of SSML elements](#supported-and-unsupported-ssml-elements-for-azure-ai-speech-hd-voices).|  Support for [a subset of SSML elements](openai-voices.md#ssml-elements-supported-by-openai-text-to-speech-voices-in-azure-ai-speech).  | Support for the [full set of SSML](speech-synthesis-markup-structure.md) in Azure AI Speech.  |