freshness

Author: PatrickFarley

articles/ai-services/autoscale.md

@@ -7,16 +7,16 @@ ms.service: azure-ai-services
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 01/10/2025
+ms.date: 06/30/2025
 ---
 
 # Autoscale AI services limits
 
-This article provides guidance for how customers can access higher rate limits on certain Azure AI services resources.
+This article provides guidance on how customers can access higher rate limits on certain Azure AI services resources.
 
 ## Overview
 
-Each Azure AI services resource has a pre-configured static call rate (transactions per second) which limits the number of concurrent calls that customers can make to the backend service in a given time frame. The autoscale feature will automatically increase/decrease a customer's resource's rate limits based on near-real-time resource usage metrics and backend service capacity metrics.
+Each Azure AI services resource has a pre-configured static call rate (transactions per second) which limits the number of concurrent calls that customers can make to the service in a given time frame. The autoscale feature will automatically increase/decrease a customer's resource's rate limits based on near-real-time resource usage metrics and backend service capacity metrics.
 
 ## Get started with the autoscale feature
 
@@ -84,7 +84,7 @@ Be aware of potential errors and their consequences. If a bug in your client app
 Yes, you can disable the autoscale feature through Azure portal or CLI and return to your default call rate limit setting. If your resource was previously approved for a higher default TPS, it goes back to that rate. It can take up to five minutes for the changes to go into effect.
 
 
-## Next steps
+## Related content
 
 * [Plan and Manage costs for Azure AI services](../ai-foundry/how-to/costs-plan-manage.md).
 * [Optimize your cloud investment with Microsoft Cost Management](/azure/cost-management-billing/costs/cost-mgt-best-practices?WT.mc_id=costmanagementcontent_docsacmhorizontal_-inproduct-learn).

articles/ai-services/computer-vision/computer-vision-how-to-install-containers.md

@@ -6,7 +6,7 @@ author: PatrickFarley
 manager: nitinme
 ms.service: azure-ai-vision
 ms.topic: how-to
-ms.date: 06/26/2024
+ms.date: 06/30/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.update-cycle: 365-days
 ms.author: pafarley
@@ -15,7 +15,7 @@ keywords: on-premises, OCR, Docker, container
 
 # Install Azure AI Vision 3.2 GA Read OCR container
 
-Containers let you run the Azure AI Vision APIs in your own environment and can help you meet specific security and data governance requirements. In this article you'll learn how to download, install, and run the Azure AI Vision Read (OCR) container.
+Containers let you run the Azure AI Vision APIs in your own environment and can help you meet specific security and data governance requirements. In this article you learn how to download, install, and run the Azure AI Vision Read (OCR) container.
 
 The Read container allows you to extract printed and handwritten text from images and documents in JPEG, PNG, BMP, PDF, and TIFF file formats. For more information on the Read service, see the [Read API how-to guide](how-to/call-read-api.md).
 
@@ -30,7 +30,7 @@ The Read 3.2 OCR container is the latest GA model and provides:
 * Support for larger documents and images.
 * Confidence scores.
 * Support for documents with both print and handwritten text.
-* Ability to extract text from only selected page(s) in a document.
+* Ability to extract text from only selected pages in a document.
 * Choose text line output order from default to a more natural reading order for Latin languages only.
 * Text line classification as handwritten style or not for Latin languages only.
 
@@ -133,7 +133,7 @@ mcr.microsoft.com/azure-cognitive-services/vision/read:3.2-model-2022-04-30
 More [examples](./computer-vision-resource-container-config.md#example-docker-commands) of the `docker run` command are available. 
 
 > [!IMPORTANT]
-> The `Eula`, `Billing`, and `ApiKey` options must be specified to run the container; otherwise, the container won't start.  For more information, see [Billing](#billing).
+> The `Eula`, `Billing`, and `ApiKey` options must be specified to run the container; otherwise, the container won't start. For more information, see [Billing](#billing).
 
 <!--If you need higher throughput (for example, when processing multi-page files), consider deploying multiple containers [on a Kubernetes cluster](deploy-computer-vision-on-premises.md), using [Azure Storage](/azure/storage/common/storage-account-create) and [Azure Queue](/azure/storage/queues/storage-queues-introduction).-->
 
@@ -143,7 +143,7 @@ To find your connection string:
 
 1. Navigate to **Storage accounts** on the Azure portal, and find your account.
 2. Select on **Access keys** in the left pane.
-3. Your connection string will be located below **Connection string**
+3. Your connection string is located below **Connection string**
 
 [!INCLUDE [Running multiple containers on the same host](../includes/cognitive-services-containers-run-multiple-same-host.md)]
 
@@ -159,9 +159,9 @@ Use the host, `http://localhost:5000`, for container APIs. You can view the Swag
 
 ### Asynchronous Read
 
-You can use the `POST /vision/v3.2/read/analyze` and `GET /vision/v3.2/read/operations/{operationId}` operations in concert to asynchronously read an image, similar to how the Azure AI Vision service uses those corresponding REST operations. The asynchronous POST method will return an `operationId` that is used as the identifier to the HTTP GET request.
+You can use the `POST /vision/v3.2/read/analyze` and `GET /vision/v3.2/read/operations/{operationId}` operations in concert to asynchronously read an image, similar to how the Azure AI Vision service uses those corresponding REST operations. The asynchronous POST method returns an `operationId` that is used as the identifier to the HTTP GET request.
 
-From the swagger UI, select the `Analyze` to expand it in the browser. Then select **Try it out** > **Choose file**. In this example, we'll use the following image:
+From the swagger UI, select the `Analyze` to expand it in the browser. Then select **Try it out** > **Choose file**. In this example, we use the following image:
 
 ![tabs vs spaces](media/tabs-vs-spaces.png)
 
@@ -174,7 +174,7 @@ When the asynchronous POST has run successfully, it returns an **HTTP 202** stat
  server: Kestrel
 ```
 
-The `operation-location` is the fully qualified URL and is accessed via an HTTP GET. Here is the JSON response from executing the `operation-location` URL from the preceding image:
+The `operation-location` is the fully qualified URL and is accessed via an HTTP GET. Here's the JSON response from executing the `operation-location` URL from the preceding image:
 
 ```json
 {
@@ -284,7 +284,7 @@ The `operation-location` is the fully qualified URL and is accessed via an HTTP
 
 
 > [!IMPORTANT]
-> If you deploy multiple Read OCR containers behind a load balancer, for example, under Docker Compose or Kubernetes, you must have an external cache. Because the processing container and the GET request container might not be the same, an external cache stores the results and shares them across containers. For details about cache settings, see [Configure Azure AI Vision Docker containers](./computer-vision-resource-container-config.md).
+> If you deploy multiple Read OCR containers behind a load balancer, for example, under Docker Compose or Kubernetes, you must have an external cache. Because the processing container and the `GET` request container might not be the same, an external cache stores the results and shares them across containers. For details about cache settings, see [Configure Azure AI Vision Docker containers](./computer-vision-resource-container-config.md).
 
 ### Synchronous read
 

articles/ai-services/computer-vision/how-to/call-analyze-image-40.md

@@ -9,7 +9,7 @@ ms.update-cycle: 365-days
 ms.author: pafarley
 ms.service: azure-ai-vision
 ms.topic: how-to
-ms.date: 06/01/2024
+ms.date: 06/30/2025
 ms.custom:  devx-track-python, devx-track-extended-java, devx-track-js
 zone_pivot_groups: programming-languages-computer-vision
 ---
@@ -49,7 +49,7 @@ This article demonstrates how to call the Image Analysis 4.0 API to return infor
 
 ::: zone-end
 
-## Next steps
+## Related content
 
 * Explore the [concept articles](../concept-describe-images-40.md) to learn more about each feature.
 * Explore the SDK code samples on GitHub:

articles/ai-services/computer-vision/includes/how-to-guides/analyze-image-40-rest.md

@@ -4,18 +4,18 @@ manager: nitinme
 ms.service: ai-services
 ms.subservice: computer-vision
 ms.topic: include
-ms.date: 08/01/2023
+ms.date: 06/30/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.update-cycle: 365-days
 ms.author: pafarley
 ---
 
 ## Prerequisites
 
-This guide assumes you have successfully followed the steps mentioned in the [quickstart](/azure/ai-services/computer-vision/quickstarts-sdk/image-analysis-client-library-40) page. This means:
+This guide assumes you've successfully followed the steps mentioned in the [quickstart](/azure/ai-services/computer-vision/quickstarts-sdk/image-analysis-client-library-40) page. This means:
 
-* You have <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesComputerVision"  title="created a Computer Vision resource"  target="_blank">created a Computer Vision resource </a> and obtained a key and endpoint URL.
-* You have successfully made a `curl.exe` call to the service (or used an alternative tool). You modify the `curl.exe` call based on the examples here.
+* You've <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesComputerVision"  title="created a Computer Vision resource"  target="_blank">created a Computer Vision resource </a> and obtained a key and endpoint URL.
+* You've successfully made a `curl.exe` call to the service (or used an alternative tool). You modify the `curl.exe` call based on the examples here.
 
 ## Authenticate against the service
 
@@ -69,7 +69,7 @@ A populated URL might look like this:
 
 ### Set model name when using a custom model
 
-You can also do image analysis with a custom trained model. To create and train a model, see [Create a custom Image Analysis model](/azure/ai-services/computer-vision/how-to/model-customization). Once your model is trained, all you need is the model's name. You do not need to specify visual features if you use a custom model.
+You can also do image analysis with a custom trained model. To create and train a model, see [Create a custom Image Analysis model](/azure/ai-services/computer-vision/how-to/model-customization). Once your model is trained, all you need is the model's name. You don't need to specify visual features if you use a custom model.
 
 
 To use a custom model, don't use the features query parameter. Instead, set the `model-name` parameter to the name of your model as shown here. Replace `MyCustomModelName` with your custom model name.
@@ -272,7 +272,7 @@ The service returns a `200` HTTP response, and the body contains the returned da
 
 ## Error codes
 
-On error, the Image Analysis service response contains a JSON payload that includes an error code and error message. It may also include other details in the form of and inner error code and message. For example:
+On error, the Image Analysis service response contains a JSON payload that includes an error code and error message. It might also include other details in the form of and inner error code and message. For example:
 
 ```json
 {

articles/ai-services/computer-vision/quickstarts-sdk/image-analysis-client-library.md

@@ -6,7 +6,7 @@ author: PatrickFarley
 manager: nitinme
 ms.service: azure-ai-vision
 ms.topic: quickstart
-ms.date: 02/27/2024
+ms.date: 06/30/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.update-cycle: 365-days
 ms.author: pafarley

articles/ai-services/computer-vision/whats-new.md

@@ -9,7 +9,7 @@ ms.custom:
   - build-2023
   - ignite-2023
 ms.topic: whats-new
-ms.date: 03/11/2024
+ms.date: 06/30/2025
 ms.collection: ce-skilling-fresh-tier2
 ms.update-cycle: 365-days
 ms.author: pafarley
@@ -51,7 +51,7 @@ For more information, see the [SDK release notes](https://github.com/Azure-Sampl
 
 ### Image Analysis 4.0 Preview API deprecation
 
-On March 31, 2025, the Image Analysis 4.0 Preview APIs will be retired. Before that date, you'll need to migrate your Azure Image Analysis workloads to the [Image Analysis 4.0 GA ](/rest/api/computervision/operation-groups?view=rest-computervision-v4.0%20(2024-02-01))API.
+On March 31, 2025, the Image Analysis 4.0 Preview APIs will be retired. Before that date, you need to migrate your Azure Image Analysis workloads to the [Image Analysis 4.0 GA ](/rest/api/computervision/operation-groups?view=rest-computervision-v4.0%20(2024-02-01))API.
 
 We encourage you to make the transition sooner to gain access to improvements such as multimodal embedding, synchronous OCR, people detection, image tagging, smart cropping, caption, dense caption, and image object detection.
 These Image Analysis 4.0 preview APIs will be retired on March 31, 2025:

articles/ai-services/content-moderator/index.yml

@@ -11,7 +11,7 @@ metadata:
   ms.topic: landing-page
   author: PatrickFarley
   ms.author: pafarley
-  ms.date: 06/12/2025
+  ms.date: 06/30/2025
 
 # linkListType: architecture | concept | deploy | download | get-started | how-to-guide | learn | overview | quickstart | reference | tutorial | video | whats-new
 

articles/ai-services/openai/concepts/safety-system-message-templates.md

@@ -4,7 +4,7 @@ titleSuffix: Azure OpenAI in Azure AI Foundry Models
 description: This article contains recommended safety system messages for your generative AI systems, to help reduce the propensity of harm in various concern areas.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 03/21/2025
+ms.date: 06/30/2025
 ms.custom:
 manager: nitinme
 author: PatrickFarley
@@ -14,12 +14,12 @@ ms.author: pafarley
 
 # Safety system message templates
 
-This article contains recommended safety system messages for your generative AI systems, to help reduce the propensity of harm in various concern areas. Before you begin evaluating and integrating your safety system messages, visit the [Safety System Message documentation](/azure/ai-services/openai/concepts/system-message) to get started.  
+This article contains recommended safety system messages for your generative AI systems to help reduce the propensity of harm in various concern areas. Before you begin evaluating and integrating your safety system messages, visit the [Safety system message conceptual guide](/azure/ai-services/openai/concepts/system-message) to get started.  
 
 > [!NOTE]
-> Using a safety system message is one of many techniques that can be used for mitigations risks in AI systems and is different from [Azure AI Content Safety](/azure/ai-services/content-safety/overview). 
+> Using a safety system message is one of many techniques that can be used for mitigations risks in AI systems and is different from the [Azure AI Content Safety](/azure/ai-services/content-safety/overview) service. 
 
-## Recommended system messages 
+## Recommended system messages
 
 Below are examples of recommended system message components you can include to potentially mitigate various harms in your AI system. 
 
@@ -50,4 +50,4 @@ The following steps show how to leverage safety system messages in [Azure AI Fou
 
 ## Evaluation 
 
-We recommend adjusting your safety system message approach based on an iterative process of identification and evaluation. Learn more in our [Safety System Message documentation](/azure/ai-services/openai/concepts/system-message). 
+We recommend you adjust your safety system message approach based on an iterative process of identification and evaluation. Learn more in the [Safety system message conceptual guide](/azure/ai-services/openai/concepts/system-message).