Merge pull request #3965 from sdgilley/sdg-left-pane

[BULK UPDATE] standardize text for left pane

Author: denrea

articles/ai-foundry/azure-openai-in-azure-ai-foundry.md

@@ -32,7 +32,7 @@ If you've been using Azure OpenAI Studio, all your work, such as your deployment
 
 :::image type="content" source="media/azure-openai-in-ai-studio/studio-home.png" alt-text="Screenshot shows the new Azure OpenAI in Azure AI Foundry portal." lightbox="media/azure-openai-in-ai-studio/studio-home.png":::
 
-Use the left navigation area to perform your tasks with Azure OpenAI models:
+Use the left pane to perform your tasks with Azure OpenAI models:
 
 * **Select models**: The **Model catalog** houses all the available Azure OpenAI models.
 

articles/ai-foundry/how-to/costs-plan-manage.md

@@ -141,7 +141,7 @@ You can also view resource group costs directly from the Azure portal. To do so:
 1. Sign in to [Azure portal](https://portal.azure.com).
 1. Select **Resource groups**. 
 1. Find and select the resource group that contains your Azure AI Foundry resources.
-1. From the left navigation menu, select **Cost analysis**.
+1. From the left pane, select **Cost analysis**.
 
     :::image type="content" source="../media/cost-management/project-costs/costs-per-resource-group.png" alt-text="Screenshot of the Azure portal cost analysis at the resource group level." lightbox="../media/cost-management/project-costs/costs-per-resource-group.png":::
 
@@ -153,9 +153,9 @@ Models deployed as a service using pay-as-you-go are offered through the Azure M
 
 1. Sign in to [Azure portal](https://portal.azure.com).
 
-1. On the left navigation area, select **Cost Management + Billing** and then, on the same menu, select **Cost Management**.
+1. On the left pane, select **Cost Management + Billing** and then, on the same menu, select **Cost Management**.
 
-1. On the left navigation area, under the section **Cost Management**, select now **Cost Analysis**.
+1. On the left pane, under the section **Cost Management**, select now **Cost Analysis**.
 
 1. Select a view such as **Resources**. The cost associated with each resource is displayed.
 

articles/ai-foundry/how-to/fine-tune-managed-compute.md

@@ -39,7 +39,7 @@ To improve model performance, you might consider fine-tuning a foundation model
 1. Sign in to [Azure AI Foundry](https://ai.azure.com).
 
 1. If you're not already in your project, select it. 
-1. Select **Fine-tuning** from the left navigation pane.
+1. Select **Fine-tuning** from the left pane.
 
     1. Select **Fine-tune model** and add the model that you want to fine-tune. This article uses _Phi-3-mini-4k-instruct_ for illustration.
     1. Select **Next** to see the available fine-tune options. Some foundation models support only the __Managed compute__ option.

articles/ai-foundry/how-to/troubleshoot-deploy-and-monitor.md

@@ -47,10 +47,10 @@ After you deployed a prompt flow, you got the error message: "Tool load failed i
 
 To fix this error, take the following steps to manually assign the ML Data scientist role to your endpoint. It might take several minutes for the new role to take effect.
 
-1. Go to your project in [Azure AI Foundry](https://ai.azure.com) and select **Management center** from the left navigation menu to open the settings page.
+1. Go to your project in [Azure AI Foundry](https://ai.azure.com) and select **Management center** from the left pane to open the settings page.
 1. Under the **Project** heading, select **Overview**.
 1. Under **Quick reference**, select the link to your resource group to open it in the Azure portal. 
-1. Select **Access control (IAM)** from the left navigation menu in the Azure portal.
+1. Select **Access control (IAM)** from the left pane in the Azure portal.
 1. Select **Add role assignment**.
 1. Select **Azure ML Data Scientist**, and select __Next__.
 1. Select **Managed Identity**.
@@ -59,7 +59,7 @@ To fix this error, take the following steps to manually assign the ML Data scien
 1. Select your endpoint's name.
 1. Select **Select**.
 1. Select **Review + Assign**.
-1. Return to your project in Azure AI Foundry portal and select **Deployments** from the left navigation menu. 
+1. Return to your project in Azure AI Foundry portal and select **Deployments** from the left pane. 
 1. Select your deployment.
 1. Test the prompt flow deployment.
 
@@ -75,10 +75,10 @@ This error message refers to a situation where the deployment build failed. You
 
 __Option 1: Find the build log for the Azure default blob storage.__
 
-1. Go to your project in [Azure AI Foundry](https://ai.azure.com) and select **Management center** from the left navigation menu to open the settings page.
+1. Go to your project in [Azure AI Foundry](https://ai.azure.com) and select **Management center** from the left pane to open the settings page.
 1. Under the **Hub** heading, select **Overview**.
 1. In the section for **Connected resources**, select the link to your storage account name. This name should be the name of the storage account listed in the error message you received. You'll be taken to the storage account page in the [Azure portal](https://portal.azure.com).
-1. On the storage account page, select **Data Storage** > **Containers** from the left navigation menu.
+1. On the storage account page, select **Data Storage** > **Containers** from the left pane.
 1. Select the container name that's listed in the error message you received.
 1. Select through folders to find the build logs.
 
@@ -88,7 +88,7 @@ __Option 2: Find the build log within Azure Machine Learning studio.__
 > This option to access the build log uses [Azure Machine Learning studio](https://ml.azure.com), which is a different portal than [Azure AI Foundry](https://ai.azure.com).
 
 1. Go to [Azure Machine Learning studio](https://ml.azure.com).
-2. Select **Endpoints** from the left navigation menu.
+2. Select **Endpoints** from the left pane.
 3. Select your endpoint name. It might be identical to your deployment name.
 4. Select the link to **Environment** from the deployment section.
 5. Select **Build log** at the top of the environment details page.

articles/ai-foundry/includes/deploy-model.md

@@ -19,8 +19,8 @@ To work with a model, you first deploy it into a project. If you don't yet have
 
         :::image type="content" source="../media/tutorials/chat/home-page.png" alt-text="Screenshot of the home page if with no projects." lightbox="../media/tutorials/chat/home-page.png":::
 
-    * If you have projects but aren't in one, select the project you wish to use, then select **Model catalog** from the left navigation pane.
-    * If you are in a project, select **Model catalog** from the left navigation pane.
+    * If you have projects but aren't in one, select the project you wish to use, then select **Model catalog** from the left pane.
+    * If you are in a project, select **Model catalog** from the left pane.
 
 1. Select the **gpt-4o-mini** model from the list of models. You can use the search bar to find it. 
 

articles/ai-foundry/includes/find-deployments.md

@@ -10,4 +10,4 @@ ms.date: 10/09/2024
 ms.custom: include, ignite-2024
 ---
 
-You can always find the endpoint's details, URL, and access keys by navigating to your project's **Management center** from the left navigation pane. Then, select **Models + endpoints**.
+You can always find the endpoint's details, URL, and access keys by navigating to your project's **Management center** from the left pane. Then, select **Models + endpoints**.

articles/ai-foundry/includes/open-catalog.md

@@ -11,4 +11,4 @@ ms.custom: include, ignite-2024
 ---
 1. Sign in to [Azure AI Foundry](https://ai.azure.com).
 1. If you’re not already in your project, select it. 
-1. Select **Model catalog** from the left navigation pane.
+1. Select **Model catalog** from the left pane.

articles/ai-foundry/model-inference/includes/configure-entra-id/portal.md

@@ -30,7 +30,7 @@ Follow these steps to configure Microsoft Entra ID for inference:
 
       :::image type="content" source="../../media/configure-entra-id/locate-resource-ai-services.png" alt-text="Screenshot showing the resource to which we configure Microsoft Entra ID." lightbox="../../media/configure-entra-id/locate-resource-ai-services.png":::
 
-2. On the left navigation bar, select **Access control (IAM)** and then select **Add** > **Add role assignment**.
+2. On the left pane, select **Access control (IAM)** and then select **Add** > **Add role assignment**.
 
    :::image type="content" source="../../media/configure-entra-id/resource-aim.png" alt-text="Screenshot showing how to add a role assignment in the Access control section of the resource in the Azure portal." lightbox="../../media/configure-entra-id/resource-aim.png":::
 

articles/ai-foundry/model-inference/includes/configure-entra-id/troubleshooting.md

@@ -11,7 +11,7 @@ Before troubleshooting, verify that you have the right permissions assigned:
 
 1. Go to the [Azure portal](https://portal.azure.com) and locate the **Azure AI Services** resource you're using.
 
-2. On the left navigation bar, select **Access control (IAM)** and then select **Check access**.
+2. On the left pane, select **Access control (IAM)** and then select **Check access**.
 
 3. Type the name of the user or identity you are using to connect to the service.
 

articles/ai-foundry/model-inference/includes/configure-project-connection/portal.md

@@ -44,7 +44,7 @@ You can see the model deployments available in the connected resource by followi
 
 1. Go to [Azure AI Foundry Portal](https://ai.azure.com).
 
-2. On the left navigation bar, select **Models + endpoints**.
+2. On the left pane, select **Models + endpoints**.
 
 3. The page displays the model deployments available to your, grouped by connection name. Locate the connection you have just created, which should be of type **Azure AI Services**.