Merge branch 'main' into main

Author: rickbartra91

.openpublishing.redirection.virtual-machines.json

@@ -4,6 +4,6 @@
             "source_path_from_root": "/articles/virtual-machines/security-recommendations.md",
             "redirect_url": "/security/benchmark/azure/baselines/virtual-machines-windows-virtual-machines-security-baseline",
             "redirect_document_id": false
-        }
+        }        
     ]
 }

articles/ai-services/openai/api-version-deprecation.md

@@ -69,9 +69,9 @@ This version contains support for the latest GA features like Whisper, DALL-E 3,
 
 We recommend first testing the upgrade to new API versions to confirm there's no impact to your application from the API update before making the change globally across your environment.
 
-If you're using the OpenAI Python client library or the REST API, you'll need to update your code directly to the latest preview API version.
+If you're using the OpenAI Python or JavaScript client libraries, or the REST API, you'll need to update your code directly to the latest preview API version.
 
-If you're using one of the Azure OpenAI SDKs for C#, Go, Java, or JavaScript you'll instead need to update to the latest version of the SDK. Each SDK release is hardcoded to work with specific versions of the Azure OpenAI API.
+If you're using one of the Azure OpenAI SDKs for C#, Go, or Java, you'll instead need to update to the latest version of the SDK. Each SDK release is hardcoded to work with specific versions of the Azure OpenAI API.
 
 ## Next steps
 

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

@@ -158,7 +158,7 @@ When you deploy a specified number of provisioned throughput units (PTUs), a set
 
 PTU deployment utilization = (PTUs consumed in the time period) / (PTUs deployed in the time period)
 
-You can find the utilization measure in the Azure-Monitor section for your resource. To access the monitoring dashboards sign-in to [https://portal.azure.com](https://portal.azure.com), go to your Azure OpenAI resource and select the Metrics page from the left nav. On the metrics page, select the 'Provisioned-managed utilization' measure. If you have more than one deployment in the resource, you should also split the values by each deployment by clicking the 'Apply Splitting' button.
+You can find the utilization measure in the Azure-Monitor section for your resource. To access the monitoring dashboards sign-in to [https://portal.azure.com](https://portal.azure.com), go to your Azure OpenAI resource and select the Metrics page from the left nav. On the metrics page, select the 'Provisioned-managed utilization V2' metric. If you have more than one deployment in the resource, you should also split the values by each deployment by clicking the 'Apply Splitting' button.
 
 :::image type="content" source="../media/provisioned/azure-monitor-utilization.jpg" alt-text="Screenshot of the provisioned managed utilization on the resource's metrics blade in the Azure portal." lightbox="../media/provisioned/azure-monitor-utilization.jpg":::
 

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

@@ -7,7 +7,7 @@ manager: nitinme
 
 ms.service: azure-ai-translator
 ms.topic: reference
-ms.date: 04/29/2024
+ms.date: 08/14/2024
 ms.author: lajanuar
 ---
 
@@ -90,7 +90,7 @@ The body of the request is a JSON array. Each array element is a JSON object wit
 The following limitations apply:
 
 * The array can have at most 100 elements.
-* The entire text included in the request can't exceed 10,000 characters including spaces.
+* The entire text included in the request can't exceed 50,000 characters including spaces.
 
 ## Response body
 
@@ -380,13 +380,13 @@ This feature works the same way with `textType=text` or with `textType=html`. Th
 
 ## Request limits
 
-Each translate request is limited to 10,000 characters, across all the target languages you're translating to. For example, sending a translate request of 3,000 characters to translate to three different languages results in a request size of 3000x3 = 9,000 characters, which satisfy the request limit. You're charged per character, not by the number of requests. We recommended sending shorter requests.
+Each translate request is limited to 50,000 characters, across all the target languages you're translating to. For example, sending a translate request of 3,000 characters to translate to three different languages results in a request size of 3000x3 = 9,000 characters, which satisfy the request limit. You're charged per character, not by the number of requests. We recommended sending shorter requests.
 
 The following table lists array element and character limits for the Translator **translation** operation.
 
 | Operation | Maximum size of array element | Maximum number of array elements | Maximum request size (characters) |
 |:----|:----|:----|:----|
-| translate | 10,000 | 100 | 10,000 |
+| translate | 10,000 | 100 | 50,000 |
 
 ## Use docker compose: Translator with supporting containers
 

articles/ai-studio/concepts/ai-resources.md

@@ -16,7 +16,7 @@ author: Blackmist
 
 # Manage, collaborate, and organize with hubs
 
-Hubs are the primary top-level Azure resource for AI studio and provide a central way for a team to govern security, connectivity, and computing resources across playgrounds and projects. Once a hub is created, developers can create projects from it and access shared company resources without needing an IT administrator's repeated help.
+Hubs are the primary top-level Azure resource for AI Studio and provide a central way for a team to govern security, connectivity, and computing resources across playgrounds and projects. Once a hub is created, developers can create projects from it and access shared company resources without needing an IT administrator's repeated help.
 
 Project workspaces that are created using a hub inherit the same security settings and shared resource access. Teams can create project workspaces as needed to organize their work, isolate data, and/or restrict access. 
 
@@ -99,7 +99,7 @@ Azure AI Studio layers on top of existing Azure services including Azure AI and
 
 [!INCLUDE [Resource provider kinds](../includes/resource-provider-kinds.md)]
 
-When you create a new hub, a set of dependent Azure resources are required to store data that you upload or get generated when working in AI studio. If not provided by you, and required, these resources are automatically created.
+When you create a new hub, a set of dependent Azure resources are required to store data that you upload or get generated when working in AI Studio. If not provided by you, and required, these resources are automatically created.
 
 [!INCLUDE [Dependent Azure resources](../includes/dependent-resources.md)]
 

articles/ai-studio/concepts/architecture.md

@@ -19,13 +19,13 @@ AI Studio provides a unified experience for AI developers and data scientists to
 
 The top level AI Studio resources (hub and project) are based on Azure Machine Learning. Connected resources, such as Azure OpenAI, Azure AI services, and Azure AI Search, are used by the hub and project in reference, but follow their own resource management lifecycle.
 
-- **AI hub**: The hub is the top-level resource in AI Studio. The Azure resource provider for a hub is `Microsoft.MachineLearningServices/workspaces`, and the kind of resource is `Hub`. It provides the following features:
+- **AI Studio hub**: The hub is the top-level resource in AI Studio. The Azure resource provider for a hub is `Microsoft.MachineLearningServices/workspaces`, and the kind of resource is `Hub`. It provides the following features:
     - Security configuration including a managed network that spans projects and model endpoints.
     - Compute resources for interactive development, finetuning, open source, and serverless model deployments.
     - Connections to other Azure services such as Azure OpenAI, Azure AI services, and Azure AI Search. Hub-scoped connections are shared with projects created from the hub.
     - Project management. A hub can have multiple child projects.
     - An associated Azure storage account for data upload and artifact storage.
-- **AI project**: A project is a child resource of the hub. The Azure resource provider for a project is `Microsoft.MachineLearningServices/workspaces`, and the kind of resource is `Project`. The project provides the following features:
+- **AI Studio project**: A project is a child resource of the hub. The Azure resource provider for a project is `Microsoft.MachineLearningServices/workspaces`, and the kind of resource is `Project`. The project provides the following features:
     - Access to development tools for building and customizing AI applications.   
     - Reusable components including datasets, models, and indexes.
     - An isolated container to upload data to (within the storage inherited from the hub).

articles/ai-studio/concepts/safety-evaluations-transparency-note.md

@@ -51,9 +51,9 @@ Azure AI Studio provisions an Azure OpenAI GPT-4 model and orchestrates adversar
 
 The safety evaluations aren't intended to use for any purpose other than to evaluate content risks and jailbreak vulnerabilities of your generative AI application:
 
-- **Evaluating your generative AI application pre-deployment**: Using the evaluation wizard in the Azure AI studio or the Azure AI Python SDK, safety evaluations can assess in an automated way to evaluate potential content or security risks.
+- **Evaluating your generative AI application pre-deployment**: Using the evaluation wizard in the Azure AI Studio or the Azure AI Python SDK, safety evaluations can assess in an automated way to evaluate potential content or security risks.
 - **Augmenting your red-teaming operations**: Using the adversarial simulator, safety evaluations can simulate adversarial interactions with your generative AI application to attempt to uncover content and security risks.
-- **Communicating content and security risks to stakeholders**: Using the Azure AI studio, you can share access to your Azure AI Studio project with safety evaluations results with auditors or compliance stakeholders.
+- **Communicating content and security risks to stakeholders**: Using the Azure AI Studio, you can share access to your Azure AI Studio project with safety evaluations results with auditors or compliance stakeholders.
 
 #### Considerations when choosing a use case
 

articles/ai-studio/how-to/create-hub-terraform.md

@@ -1,6 +1,6 @@
 ---
 title: 'Use Terraform to create an Azure AI Studio hub'
-description: In this article, you create an Azure AI hub, an AI project, an AI services resource, and more resources.
+description: In this article, you create an Azure AI Studio hub, an Azure AI Studio project, an AI services resource, and more resources.
 ms.topic: how-to
 ms.date: 07/12/2024
 titleSuffix: Azure AI Studio 
@@ -27,8 +27,8 @@ In this article, you use Terraform to create an Azure AI Studio hub, a project,
 > * Set up a storage account
 > * Establish a key vault
 > * Configure AI services
-> * Build an Azure AI hub
-> * Develop an AI project
+> * Build an AI Studio hub
+> * Develop an AI Studio project
 > * Establish an AI services connection
 
 ## Prerequisites

articles/ai-studio/how-to/create-projects.md

@@ -63,7 +63,7 @@ Use the following tabs to select the method you plan to use to create a project:
 
     For more information on authenticating, see [Authentication methods](/cli/azure/authenticate-azure-cli).
 
-1. Once the extension is installed and authenticated to your Azure subscription, use the following command to create a new Azure AI project from an existing Azure AI hub:
+1. Once the extension is installed and authenticated to your Azure subscription, use the following command to create a new Azure AI Studio project from an existing Azure AI Studio hub:
 
     ```azurecli
     az ml workspace create --kind project --hub-id {my_hub_ARM_ID} --resource-group {my_resource_group} --name {my_project_name}
@@ -122,7 +122,7 @@ In addition, a number of resources are only accessible by users in your project
    | workspacefilestore | {project-GUID}-code | Hosts files created on your compute and using prompt flow |
 
 > [!NOTE]
-> Storage connections are not created directly with the project when your storage account has public network access set to disabled. These are created instead when a first user accesses AI studio over a private network connection. [Troubleshoot storage connections](troubleshoot-secure-connection-project.md#troubleshoot-missing-storage-connections)
+> Storage connections are not created directly with the project when your storage account has public network access set to disabled. These are created instead when a first user accesses AI Studio over a private network connection. [Troubleshoot storage connections](troubleshoot-secure-connection-project.md#troubleshoot-missing-storage-connections)
 
 
 ## Next steps

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

@@ -548,19 +548,19 @@ In this section, you create an endpoint with the name **meta-llama3-8b-qwerty**.
 
 ## Use the serverless API endpoint
 
-Models deployed in Azure Machine Learning and Azure AI studio in Serverless API endpoints support the [Azure AI Model Inference API](../reference/reference-model-inference-api.md) that exposes a common set of capabilities for foundational models and that can be used by developers to consume predictions from a diverse set of models in a uniform and consistent way. 
+Models deployed in Azure Machine Learning and Azure AI Studio in Serverless API endpoints support the [Azure AI Model Inference API](../reference/reference-model-inference-api.md) that exposes a common set of capabilities for foundational models and that can be used by developers to consume predictions from a diverse set of models in a uniform and consistent way. 
 
 Read more about the [capabilities of this API](../reference/reference-model-inference-api.md#capabilities) and how [you can use it when building applications](../reference/reference-model-inference-api.md#getting-started). 
 
 ## Network isolation
 
 Endpoints for models deployed as Serverless APIs follow the public network access (PNA) flag setting of the AI Studio Hub that has the project in which the deployment exists. To secure your MaaS endpoint, disable the PNA flag on your AI Studio Hub. You can secure inbound communication from a client to your endpoint by using a private endpoint for the hub.
 
-To set the PNA flag for the Azure AI hub:
+To set the PNA flag for the Azure AI Studio hub:
 
 1. Go to the [Azure portal](https://portal.azure.com).
-2. Search for the Resource group to which the hub belongs, and select your Azure AI hub from the resources listed for this Resource group.
-3. On the hub Overview page, use the left navigation pane to go to Settings > Networking.
+2. Search for the Resource group to which the hub belongs, and select the **Azure AI hub** from the resources listed for this resource group.
+3. From the hub **Overview** page on the left menu, select **Settings** > **Networking**.
 4. Under the **Public access** tab, you can configure settings for the public network access flag.
 5. Save your changes. Your changes might take up to five minutes to propagate.