Merge pull request #6766 from msakande/freshness-troubleshoot-deploy-and-monitor

freshness review for deployment troubleshooting

Author: JamesJBarnett

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

@@ -1,20 +1,23 @@
 ---
 title: How to troubleshoot your deployments and monitors in Azure AI Foundry portal
 titleSuffix: Azure AI Foundry
-description: This article provides instructions on how to troubleshoot your deployments and monitors in Azure AI Foundry portal.
+description: Learn how to troubleshoot and monitor model deployments in Azure AI Foundry portal to quickly resolve errors and optimize performance.
 ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2023
   - build-2024
   - ignite-2024
   - hub-only
+ai-usage: ai-assisted
 ms.topic: how-to
-ms.date: 04/23/2025
+ms.date: 08/26/2025
 ms.reviewer: fasantia
 reviewer: santiagxf
 ms.author: mopeakande
 manager: nitinme
 author: msakande
+
+#CustomerIntent: As a developer or data scientist, I want to troubleshoot and monitor model deployments in Azure AI Foundry so that I can quickly resolve errors, optimize performance, and ensure my AI solutions run smoothly.
 ---
 
 # How to troubleshoot your deployments and monitors in Azure AI Foundry portal
@@ -25,7 +28,7 @@ This article provides instructions on how to troubleshoot your deployments and m
 
 ## Deployment issues
 
-For the general deployment error code reference, see [Troubleshooting online endpoints deployment and scoring](/azure/machine-learning/how-to-troubleshoot-online-endpoints) in the Azure Machine Learning documentation. Much of the information there also apply to Azure AI Foundry deployments.
+For general deployment error code reference, see [Troubleshooting online endpoints deployment and scoring](/azure/machine-learning/how-to-troubleshoot-online-endpoints) in the Azure Machine Learning documentation. Much of the information there also applies to Azure AI Foundry deployments.
 
 
 ### Error: Use of Azure OpenAI models in Azure Machine Learning requires Azure OpenAI in Azure AI Foundry Models resources
@@ -44,26 +47,27 @@ For more information about managing quota, see:
 
 ### Error: `ToolLoadError`
 
-After you deployed a prompt flow, you got the error message: "Tool load failed in 'search_question_from_indexed_docs': (ToolLoadError) Failed to load package tool 'Vector Index Lookup': (HttpResponseError) (AuthorizationFailed)." 
+After you deploy a prompt flow, you might get the error message: "Tool load failed in 'search_question_from_indexed_docs': (ToolLoadError) Failed to load package tool 'Vector Index Lookup': (HttpResponseError) (AuthorizationFailed)." 
 
-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.
+To fix this error, manually assign the **Azure ML Data Scientist** role to your endpoint by following these steps. It might take several minutes for the new role to take effect.
 
 [!INCLUDE [uses-hub-only](../includes/uses-hub-only.md)]
 
 1. Go to your project in [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) 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. Under **Project properties**, select the link to your resource group to open it 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**.
-1. Select **+ Select members**.
-1. Select **Machine Learning Online Endpoints** in the Managed Identity dropdown field.
-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 pane. 
-1. Select your deployment.
+1. Select the **Azure ML Data Scientist** role. You might have to search for it in the search box.
+1. Select **Next** to go to the **Members** page.
+1. For **Assign access to**, select **Managed Identity**.
+1. For **Members**, select **+ Select members**. This action opens up the right pane where you can select managed identities.
+    1. Select **Machine learning online endpoint** in the Managed identity dropdown field.
+    1. Select your endpoint's name.
+    1. Select **Select** to choose the endpoint and close the right pane.
+1. Select **Review + assign**. Then select **Review + assign** again to confirm the role assignment.
+1. Return to your project in Azure AI Foundry portal and select **Models + endpoints** from the left pane.
+1. On the **Model deployments** page, select your deployment.
 1. Test the prompt flow deployment.
 
 ### Error: Deployment failure
@@ -72,17 +76,19 @@ The full error message is as follows:
 
 "ResourceNotFound: Deployment failed due to timeout while waiting for Environment Image to become available. Check Environment Build Log in ML Studio Workspace or Workspace storage for potential failures. Image build summary: [N/A]. Environment info: Name: CliV2AnonymousEnvironment, Version: 'Ver', you might be able to find the build log under the storage account 'NAME' in the container 'CONTAINER_NAME' at the Path 'PATH/PATH/image_build_aggregate_log.txt'."
 
-You might have come across an `ImageBuildFailure` error: This error happens when the environment (docker image) is being built. For more information about the error, you can check the build log for your `<CONTAINER NAME>` environment. 
+You might come across an `ImageBuildFailure` error. This error happens when the environment (docker image) is being built. For more information about the error, you can check the build log for your `<CONTAINER NAME>` environment. 
 
 This error message refers to a situation where the deployment build failed. You want to read the build log to troubleshoot further. There are two ways to access the build log.
 
 __Option 1: Find the build log for the Azure default blob storage.__
 
 [!INCLUDE [uses-hub-only](../includes/uses-hub-only.md)]
 
-1. Go to your project in [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) 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. Go to your project in [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) and select **Management center** from the left pane to open the overview page of your hub.
+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. 
+1. On the details page of the storage account, select **View in Azure Portal** to open up the storage account page in the [Azure portal](https://portal.azure.com).
+1. Alternatively, go to the Azure portal, and from the home page, select **Storage accounts** from the list of Azure services.
+1. Select your storage account from the list. You might want to search for it in the search box to find it quickly.
 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.
@@ -93,24 +99,25 @@ __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/?cid=learnDocs).
 
 1. Go to [Azure Machine Learning studio](https://ml.azure.com).
-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.
+1. Go to your workspace or hub.
+1. Select **Endpoints** from the left pane.
+1. Select your endpoint name. It might be identical to your deployment name.
+1. Select the link to **Environment** from the deployment section.
+1. Select **Build log** at the top of the environment details page.
 
 ### Error: `UserErrorFromQuotaService`
 
 The full error message is: "UserErrorFromQuotaService: Simultaneous count exceeded for subscription."
 
-This error message means that the shared quota pool has reached the maximum number of requests it can handle. Try again at a later time when the shared quota is freed up for use.
+This error message means that the shared quota pool reached the maximum number of requests it can handle. Try again later when the shared quota is freed up for use.
 
 ### Question: I deployed a web app but I don't see a way to launch it or find it
 
-We're working on improving the user experience of web app deployment at this time. In the meantime, here's a tip: if your web app launch button doesn't become active after a while, try to deploy it again, using the __update an existing app__ option. If the web app was properly deployed, it should show up on the dropdown list of your existing web apps.
+We're working on improving the user experience of web app deployment. In the meantime, here's a tip: if your web app launch button doesn't become active after a while, try to deploy it again, using the **update an existing app** option. If you properly deploy the web app, it appears on the dropdown list of your existing web apps.
 
 ### Question: I deployed a model but I don't see it in the playground
 
-Playground only supports select models, such as Azure OpenAI models and Llama-2. If playground support is available, you see the **Open in playground** button on the model deployment's **Details** page. 
+The playground only supports select models, such as Azure OpenAI models and Llama-2. If the playground supports a model, you see the **Open in playground** button on the model deployment's **Details** page. 
 
 ## Related content
 

articles/ai-foundry/includes/uses-hub-only.md

@@ -11,4 +11,4 @@ ms.custom: include
 ---
 
 > [!NOTE]
-> You must use a **[!INCLUDE [hub](hub-project-name.md)]** for this feature. A **[!INCLUDE [fdp](fdp-project-name.md)]** isn't supported. See [How do I know which type of project I have?](../what-is-azure-ai-foundry.md#how-do-i-know) and [Create a hub-based project](../how-to/create-projects.md?pivots="hub-project").
+> You must use a **[!INCLUDE [hub](hub-project-name.md)]** for this feature. An **[!INCLUDE [fdp](fdp-project-name.md)]** isn't supported. See [How do I know which type of project I have?](../what-is-azure-ai-foundry.md#how-do-i-know) and [Create a hub-based project](../how-to/create-projects.md?pivots="hub-project").