rm content filtering preview tag

Author: PatrickFarley

articles/ai-foundry/concepts/model-catalog-content-safety.md

@@ -22,19 +22,19 @@ In this article, learn about Guardrails & controls capabilities for models from
 
 ## Content filter defaults
 
-Azure AI uses a default configuration of [Azure AI Content Safety](/azure/ai-services/content-safety/overview) content filters to detect harmful content across four categories including hate and fairness, self-harm, sexual, and violence for models deployed via serverless APIs. To learn more about content filtering (preview), see [Understand harm categories](#understand-harm-categories).
+Azure AI uses a default configuration of [Azure AI Content Safety](/azure/ai-services/content-safety/overview) content filters to detect harmful content across four categories including hate and fairness, self-harm, sexual, and violence for models deployed via serverless APIs. To learn more about content filtering, see [Understand harm categories](#understand-harm-categories).
 
 The default content filtering configuration for text models is set to filter at the medium severity threshold, filtering any detected content at this level or higher. For image models, the default content filtering configuration is set at the low configuration threshold, filtering at this level or higher. For models deployed using the [Azure AI model inference service](../../ai-foundry/model-inference/how-to/configure-content-filters.md), you can create configurable filters by selecting the **Content filters** tab within the **Guardrails & controls** page of the Azure AI Foundry portal.
 
 > [!TIP]
-> Content filtering (preview) isn't available for certain model types that are deployed via serverless APIs. These model types include embedding models and time series models.
+> Content filtering isn't available for certain model types that are deployed via serverless APIs. These model types include embedding models and time series models.
 
-Content filtering (preview) occurs synchronously as the service processes prompts to generate content. You might be billed separately according to [Azure AI Content Safety pricing](https://azure.microsoft.com/pricing/details/cognitive-services/content-safety/) for such use. You can disable content filtering (preview) for individual serverless endpoints either:
+Content filtering occurs synchronously as the service processes prompts to generate content. You might be billed separately according to [Azure AI Content Safety pricing](https://azure.microsoft.com/pricing/details/cognitive-services/content-safety/) for such use. You can disable content filtering for individual serverless endpoints either:
 
 - When you first deploy a language model
 - Later, by selecting the content filtering toggle on the deployment details page
 
-Suppose you decide to use an API other than the [Azure AI Model Inference API](/azure/ai-studio/reference/reference-model-inference-api) to work with a model that is deployed via a serverless API. In such a situation, content filtering (preview) isn't enabled unless you implement it separately by using Azure AI Content Safety. To get started with Azure AI Content Safety, see [Quickstart: Analyze text content](/azure/ai-services/content-safety/quickstart-text). You run a higher risk of exposing users to harmful content if you don't use content filtering (preview) when working with models that are deployed via serverless APIs.
+Suppose you decide to use an API other than the [Azure AI Model Inference API](/azure/ai-studio/reference/reference-model-inference-api) to work with a model that is deployed via a serverless API. In such a situation, content filtering isn't enabled unless you implement it separately by using Azure AI Content Safety. To get started with Azure AI Content Safety, see [Quickstart: Analyze text content](/azure/ai-services/content-safety/quickstart-text). You run a higher risk of exposing users to harmful content if you don't use content filtering when working with models that are deployed via serverless APIs.
 
 [!INCLUDE [content-safety-harm-categories](../includes/content-safety-harm-categories.md)]
 
@@ -44,6 +44,6 @@ Pricing details are viewable at [Azure AI Content Safety pricing](https://azure.
 
 ## Related content
 
-- [How to configure content filters (preview) for models in Azure AI services](../../ai-foundry/model-inference/how-to/configure-content-filters.md)
+- [How to configure content filters for models in Azure AI services](../../ai-foundry/model-inference/how-to/configure-content-filters.md)
 - [What is Azure AI Content Safety?](../../ai-services/content-safety/overview.md)
 - [Model catalog and collections in Azure AI Foundry portal](../how-to/model-catalog-overview.md)

articles/ai-foundry/how-to/concept-data-privacy.md

@@ -47,7 +47,7 @@ The model processes your input prompts and generates outputs based on its functi
 
 Microsoft acts as the data processor for prompts and outputs sent to, and generated by, a model deployed for pay-as-you-go inferencing (MaaS). Microsoft doesn't share these prompts and outputs with the model provider. Also, Microsoft doesn't use these prompts and outputs to train or improve Microsoft models, the model provider's models, or any third party's models.
 
-Models are stateless, and they don't store any prompts or outputs. If content filtering (preview) is enabled, the Azure AI Content Safety service screens prompts and outputs for certain categories of harmful content in real time. [Learn more about how Azure AI Content Safety processes data](/legal/cognitive-services/content-safety/data-privacy).
+Models are stateless, and they don't store any prompts or outputs. If content filtering is enabled, the Azure AI Content Safety service screens prompts and outputs for certain categories of harmful content in real time. [Learn more about how Azure AI Content Safety processes data](/legal/cognitive-services/content-safety/data-privacy).
 
 Prompts and outputs are processed within the geography specified during deployment, but they might be processed between regions within the geography for operational purposes. Operational purposes include performance and capacity management.
 

articles/ai-foundry/how-to/deploy-models-gretel-navigator.md

@@ -235,7 +235,7 @@ result = client.complete(
 
 ### Apply Guardrails and controls
 
-The Azure AI model inference API supports [Azure AI Content Safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI Content Safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering (preview) system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
+The Azure AI model inference API supports [Azure AI Content Safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI Content Safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
 
 The following example shows how to handle events when the model detects harmful content in the input prompt and the filter is enabled.
 
@@ -479,7 +479,7 @@ The following example request shows other parameters that you can specify in the
 
 ### Apply Guardrails & controls
 
-The Azure AI model inference API supports [Azure AI Content Safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI Content Safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering (preview) system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
+The Azure AI model inference API supports [Azure AI Content Safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI Content Safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
 
 The following example shows how to handle events when the model detects harmful content in the input prompt.
 

articles/ai-foundry/how-to/deploy-models-serverless.md

@@ -332,7 +332,7 @@ In this section, you create an endpoint with the name **meta-llama3-8b-qwerty**.
 
         :::image type="content" source="../media/deploy-monitor/serverless/deployment-name.png" alt-text="A screenshot showing how to specify the name of the deployment you want to create." lightbox="../media/deploy-monitor/serverless/deployment-name.png":::
        > [!TIP]
-       > The **Content filter (preview)** option is enabled by default. Leave the default setting for the service to detect harmful content such as hate, self-harm, sexual, and violent content. For more information about content filtering (preview), see [Content filtering in Azure AI Foundry portal](../concepts/content-filtering.md).
+       > The **Content filter** option is enabled by default. Leave the default setting for the service to detect harmful content such as hate, self-harm, sexual, and violent content. For more information about content filtering, see [Content filtering in Azure AI Foundry portal](../concepts/content-filtering.md).
 
     1. Select **Deploy**. Wait until the deployment is ready and you're redirected to the Deployments page.
 

articles/ai-foundry/includes/content-safety-serverless-models.md

@@ -13,16 +13,16 @@ ms.custom: include file
 # Also used in Azure Machine Learning documentation
 ---
 
-For language models deployed via standard deployment, Azure AI implements a default configuration of [Azure AI Content Safety](../../ai-services/content-safety/overview.md) text moderation filters that detect harmful content such as hate, self-harm, sexual, and violent content. To learn more about content filtering (preview), see [Guardrails & controls for models curated by Azure AI in the model catalog](../concepts/model-catalog-content-safety.md). 
+For language models deployed via standard deployment, Azure AI implements a default configuration of [Azure AI Content Safety](../../ai-services/content-safety/overview.md) text moderation filters that detect harmful content such as hate, self-harm, sexual, and violent content. To learn more about content filtering, see [Guardrails & controls for models curated by Azure AI in the model catalog](../concepts/model-catalog-content-safety.md). 
 
 > [!TIP]
-> Content filtering (preview) is not available for certain model types that are deployed via serverless APIs. These model types include embedding models and time series models.
+> Content filtering is not available for certain model types that are deployed via serverless APIs. These model types include embedding models and time series models.
 
-Content filtering (preview) occurs synchronously as the service processes prompts to generate content. You might be billed separately according to [Azure AI Content Safety pricing](https://azure.microsoft.com/pricing/details/cognitive-services/content-safety/) for such use. You can disable content filtering (preview) for individual serverless endpoints either:
+Content filtering occurs synchronously as the service processes prompts to generate content. You might be billed separately according to [Azure AI Content Safety pricing](https://azure.microsoft.com/pricing/details/cognitive-services/content-safety/) for such use. You can disable content filtering for individual serverless endpoints either:
 
 - At the time when you first deploy a language model
 - Later, by selecting the content filtering toggle on the deployment details page
 
-Suppose you decide to use an API other than the [Azure AI Model Inference API](/azure/ai-studio/reference/reference-model-inference-api) to work with a model that's deployed via a serverless API. In such a situation, content filtering (preview) isn't enabled unless you implement it separately by using Azure AI Content Safety.
+Suppose you decide to use an API other than the [Azure AI Model Inference API](/azure/ai-studio/reference/reference-model-inference-api) to work with a model that's deployed via a serverless API. In such a situation, content filtering isn't enabled unless you implement it separately by using Azure AI Content Safety.
 
-To get started with Azure AI Content Safety, see [Quickstart: Analyze text content](/azure/ai-services/content-safety/quickstart-text). If you don't use content filtering (preview) when working with models that are deployed via serverless APIs, you run a higher risk of exposing users to harmful content.
+To get started with Azure AI Content Safety, see [Quickstart: Analyze text content](/azure/ai-services/content-safety/quickstart-text). If you don't use content filtering when working with models that are deployed via serverless APIs, you run a higher risk of exposing users to harmful content.

articles/ai-foundry/model-inference/how-to/configure-content-filters.md

@@ -1,5 +1,5 @@
 ---
-title: 'How to configure content filters (preview) for models in Azure AI Foundry'
+title: 'How to configure content filters for models in Azure AI Foundry'
 titleSuffix: Azure AI Foundry
 description: Learn to use and configure the content filters that come with Azure AI Foundry, including getting approval for gated modifications.
 manager: nitinme
@@ -13,7 +13,7 @@ ms.custom: ignite-2024, github-universe-2024
 zone_pivot_groups: azure-ai-models-deployment
 ---
 
-# How to configure content filters (preview) for models in Azure AI Foundry
+# How to configure content filters for models in Azure AI Foundry
 
 ::: zone pivot="ai-foundry-portal"
 [!INCLUDE [portal](../includes/configure-content-filters/portal.md)]