@@ -106,7 +106,7 @@ In this quickstart, use the following features to analyze and extract data and v
 
     :::image type="content" source="../../media/quickstarts/select-doc-intel-nuget-package.png" alt-text="Screenshot of select NuGet prerelease package window in Visual Studio.":::
 
-1. Select the **Browse** tab and type *Azure.AI.FormRecognizer*.
+1. Select the **Browse** tab and type *Azure.AI.DocumentIntelligence*.
 
 1. Select the **`Include prerelease`** checkbox.
 

@@ -154,8 +154,6 @@ This category contains the following entities:
         Any numeric or alphanumeric identifier that could contain any PII information. 
         Examples:   
             * Case Number 
-            * Driver's license
-            * Medicare Beneficiary Identifier (MBI)
             * Member Number 
             * Ticket number 
             * Bank account number 

@@ -15,9 +15,17 @@ ms.author: jboback
 
 Azure AI Language is updated on an ongoing basis. To stay up-to-date with recent developments, this article provides you with information about new releases and features.
 
+## November 2024
+
+* [Native document support](native-document-support/use-native-documents.md) is now available in public preview `2024-11-15-preview` without gated preview limitations.
+
+## October 2024
+
+* Custom language service features enable you to deploy your project to multiple [resources within a single region](concepts/custom-features/multi-region-deployment.md) via the API, so that you can use your custom model wherever you need.
+
 ## September 2024
 
-* PII detection now has container support. See more details in the Azure Update post: [Announcing Text PII Redaction Container Release](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/announcing-text-pii-redaction-container-release/ba-p/4264655).
+* PII detection now has container support. See more details in the Azure Update post: [Announcing Text PII Redaction Container Release](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/announcing-text-pii-redaction-container-release/4264655).
 * Custom sentiment analysis (preview) will be retired on January 10th, 2025. Please transition to other custom model training services, such as custom text classification in Azure AI Language, by that date.  See more details in the Azure Update post: [Retirement: Announcing upcoming retirement of custom sentiment analysis (preview) in Azure AI Language (microsoft.com)](https://azure.microsoft.com/updates/v2/custom-sentiment-analysis-retirement).
 * Custom text analytics for health (preview) will be retired on January 10th, 2025. Please transition to other custom model training services, such as custom named entity recognition in Azure AI Language, by that date.  See more details in the Azure Update post: [Retirement: Announcing upcoming retirement of custom text analytics for health (preview) in Azure AI Language (microsoft.com)](https://azure.microsoft.com/updates/v2/custom-text-analytics-for-health-retirement).
 
@@ -27,7 +35,7 @@ Azure AI Language is updated on an ongoing basis. To stay up-to-date with recent
 
 ## July 2024
 
-* [Conversational PII redaction](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/announcing-conversational-pii-detection-service-s-general/ba-p/4162881) service in English-language contexts is now Generally Available (GA).
+* [Conversational PII redaction](https://techcommunity.microsoft.com/blog/ai-azure-ai-services-blog/announcing-conversational-pii-detection-service-s-general/4162881) service in English-language contexts is now Generally Available (GA).
 * Conversation Summarization now supports 12 additional languages in preview as listed [here](summarization/language-support.md).
 * Summarization Meeting or Conversation Chapter titles features will now support reduced length to focus on the key topics.
 * Enable support for data augmentation for diacritics to generate variations of training data for diacritic variations used in some natural languages which is especially useful for Germanic and Slavic languages. 

@@ -14,6 +14,8 @@ author: s-polly
 ---
 # Data, privacy, and security for use of models through the model catalog in AI Studio
 
+[!INCLUDE [feature-preview](../includes/feature-preview.md)]
+
 This article describes how the data that you provide is processed, used, and stored when you deploy models from the model catalog. Also see the [Microsoft Products and Services Data Protection Addendum](https://aka.ms/DPA), which governs data processing by Azure services.
 
 > [!IMPORTANT]
@@ -43,8 +45,6 @@ When you deploy a model from the model catalog (base or fine-tuned) by using ser
 
 The model processes your input prompts and generates outputs based on its functionality, as described in the model details. Your use of the model (along with the provider's accountability for the model and its outputs) is subject to the license terms for the model. Microsoft provides and manages the hosting infrastructure and API endpoint. The models hosted in this *model as a service* (MaaS) scenario are subject to Azure data, privacy, and security commitments. [Learn more about Azure compliance offerings applicable to Azure AI Studio](https://servicetrust.microsoft.com/DocumentPage/7adf2d9e-d7b5-4e71-bad8-713e6a183cf3).
 
-[!INCLUDE [feature-preview](../includes/feature-preview.md)]
-
 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).

@@ -83,7 +83,7 @@ llm = AzureAICompletionsModel(
 ```
 
 > [!TIP]
-> If your model is an OpenAI model deployed to Azure OpenAI service or AI services resource, configure the client as indicated at [Azure OpenAI models and Azure AI model inference service](#azure-openai-models-and-azure-ai-model-infernece-service).
+> If your model deployment is hosted in Azure OpenAI service or Azure AI Services resource, configure the client as indicated at [Azure OpenAI models and Azure AI model inference service](#azure-openai-models-and-azure-ai-model-inference-service).
 
 If your endpoint is serving more than one model, like with the [Azure AI model inference service](../../ai-services/model-inference.md) or [GitHub Models](https://github.com/marketplace/models), you have to indicate `model_name` parameter:
 
@@ -128,23 +128,36 @@ llm = AzureAICompletionsModel(
 )
 ```
 
-### Azure OpenAI models and Azure AI model infernece service
+### Azure OpenAI models and Azure AI model inference service
 
-If you are using Azure OpenAI models or [Azure AI model inference service](../../ai-services/model-inference.md), ensure you have at least version `0.2.4` of the LlamaIndex integration. Use `api_version` parameter in case you need to select a specific `api_version`. For the [Azure AI model inference service](../../ai-services/model-inference.md), you need to pass `model_name` parameter:
+If you are using Azure OpenAI service or [Azure AI model inference service](../../ai-services/model-inference.md), ensure you have at least version `0.2.4` of the LlamaIndex integration. Use `api_version` parameter in case you need to select a specific `api_version`. 
+
+For the [Azure AI model inference service](../../ai-services/model-inference.md), you need to pass `model_name` parameter:
 
 ```python
 from llama_index.llms.azure_inference import AzureAICompletionsModel
 
 llm = AzureAICompletionsModel(
-    endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
+    endpoint="https://<resource>.services.ai.azure.com/models",
+    credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
+    model_name="mistral-large-2407",
+)
+```
+
+For Azure OpenAI service:
+
+```python
+from llama_index.llms.azure_inference import AzureAICompletionsModel
+
+llm = AzureAICompletionsModel(
+    endpoint="https://<resource>.openai.azure.com/openai/deployments/<deployment-name>",
     credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    model_name="gpt-4o",
     api_version="2024-05-01-preview",
 )
 ```
 
 > [!TIP]
-> Using a wrong `api_version` or one not supported by the model results in a `ResourceNotFound` exception.
+> Check which is the API version that your deployment is using. Using a wrong `api_version` or one not supported by the model results in a `ResourceNotFound` exception.
 
 ### Inference parameters
 

@@ -10,17 +10,24 @@ ms.date: 5/21/2024
 ms.custom: include, build-2024
 ---
 
+> [!NOTE]
+> A hub in Azure AI Studio is a one-stop shop where you manage everything your AI project needs, like security and resources, so you can develop and test faster. To learn more about how hubs can help you, see the [Hubs and projects overview](/azure/ai-studio/concepts/ai-resources) article.
+
 To create a hub in [Azure AI Studio](https://ai.azure.com), follow these steps:
 
 1. Go to the **Home** page in [Azure AI Studio](https://ai.azure.com) and sign in with your Azure account.
-1. Select **All hubs** from the left pane and then select **+ New hub**.
+1. Select **All resources** on the left pane. If you cannot see this option, in the top bar select **All resources & projects**. Then select **+ New hub**.
 
     :::image type="content" source="../media/how-to/hubs/hub-new.png" alt-text="Screenshot of the button to create a new hub." lightbox="../media/how-to/hubs/hub-new.png":::
 
-1. In the **Create a new hub** dialog, enter a name for your hub (such as *contoso-hub*) and select **Next**. Leave the default **Connect Azure AI Services** option selected. A new AI services connection is created for the hub.
+1. In the **Create a new hub** dialog, enter a name for your hub (such as *contoso-hub*). If you don't have a resource group, a new **Resource group** will be created linked to the **Subscription** provided. Leave the default **Connect Azure AI Services** option selected. 
+1. Select **Next**. If you didn't reuse an existing resource group, a new resource group (*rg-contoso*) is created.  Also an Azure AI service (*ai-contoso-hub*) is created for the hub.
 
     :::image type="content" source="../media/how-to/hubs/hub-new-connect-services.png" alt-text="Screenshot of the dialog to connect services while creating a new hub." lightbox="../media/how-to/hubs/hub-new-connect-services.png":::
 
+    > [!NOTE]
+    > If you don't see (new) before the **Resource group** and **Connect Azure AI Services** entries then an existing resource is being used. For the purposes of this tutorial, create a seperate entity via **Create new resource group** and **Create new AI Services**. This will allow you to prevent any unexpected charges by deleting the entities after the tutorial.
+
 1. Review the information and select **Create**.
 
     :::image type="content" source="../media/how-to/hubs/hub-new-review-create.png" alt-text="Screenshot of the dialog to review the settings for the new hub." lightbox="../media/how-to/hubs/hub-new-review-create.png":::