@@ -1,18 +1,18 @@
 ---
-title:  Interpret and improve model accuracy and analysis confidence scores
+title:  Interpret and improve model accuracy and confidence scores
 titleSuffix: Azure AI services
-description: Best practices to interpret the accuracy score from the train model operation and the confidence score from analysis operations.
+description: Best practices to interpret and improve Azure AI Document Intelligence accuracy scores from train model operations and confidence scores from analysis operations.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 11/19/2024
+ms.date: 02/21/2025
 ms.author: lajanuar
 ---
 
 <!-- markdownlint-disable MD033 -->
 
-# Interpret and improve model accuracy and analysis confidence scores
+# Interpret and improve accuracy and confidence scores
 
 A confidence score indicates probability by measuring the degree of statistical certainty that the extracted result is detected correctly. The estimated accuracy is calculated by running a few different combinations of the training data to predict the labeled values. In this article, learn to interpret accuracy and confidence scores and best practices for using those scores to improve accuracy and confidence results.
 

@@ -177,7 +177,7 @@ String documentUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-
 String modelId = "prebuilt-read";
 
 SyncPoller < OperationResult, AnalyzeResult > analyzeLayoutResultPoller =
-  client.beginAnalyzeDocument(modelId, invoiceUrl);;
+  client.beginAnalyzeDocument(modelId, invoiceUrl);
 
 AnalyzeResult analyzeLayoutResult = analyzeLayoutResultPoller.getFinalResult().getAnalyzeResult();
 
@@ -338,7 +338,7 @@ String modelId = "prebuilt-document";
 SyncPoller < OperationResult, AnalyzeResult > analyzeDocumentPoller =
   client.beginAnalyzeDocument(modelId, generalDocumentUrl);
 
-AnalyzeResult analyzeResult = analyzeDocumentPoller.getFinalResult().getAnalyzeResult();;
+AnalyzeResult analyzeResult = analyzeDocumentPoller.getFinalResult().getAnalyzeResult();
 
 // pages
 analyzeResult.getPages().forEach(documentPage -> {

@@ -48,7 +48,7 @@ zone_pivot_groups: programming-languages-text-analytics
 
 ::: zone-end
 
-::: zone pivot="azure-ai-foundry"
+::: zone pivot="ai-foundry-portal"
 
 [!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
 

@@ -48,7 +48,7 @@ zone_pivot_groups: programming-languages-text-analytics
 
 ::: zone-end
 
-::: zone pivot="azure-ai-foundry"
+::: zone pivot="ai-foundry-portal"
 
 [!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
 

@@ -48,7 +48,7 @@ zone_pivot_groups: programming-languages-text-analytics
 
 ::: zone-end
 
-::: zone pivot="azure-ai-foundry"
+::: zone pivot="ai-foundry-portal"
 
 [!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
 

@@ -50,7 +50,7 @@ zone_pivot_groups: programming-languages-text-analytics
 
 ::: zone-end
 
-::: zone pivot="azure-ai-foundry"
+::: zone pivot="ai-foundry-portal"
 
 [!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
 

@@ -48,7 +48,7 @@ zone_pivot_groups: programming-languages-text-analytics
 
 ::: zone-end
 
-::: zone pivot="azure-ai-foundry"
+::: zone pivot="ai-foundry-portal"
 
 [!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
 

@@ -49,7 +49,7 @@ zone_pivot_groups: programming-languages-text-analytics
 
 ::: zone-end
 
-::: zone pivot="azure-ai-foundry"
+::: zone pivot="ai-foundry-portal"
 
 [!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
 

@@ -51,7 +51,7 @@ This article contains Text Analytics for health quickstarts that help with using
 
 ::: zone-end
 
-::: zone pivot="azure-ai-foundry"
+::: zone pivot="ai-foundry-portal"
 
 [!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
 

@@ -68,6 +68,8 @@ The following table lists the timelines for models that are on track for retirem
 | Model provider | Model | Legacy date (UTC) | Deprecation date (UTC) | Retirement date (UTC) | Suggested replacement model |
 | ---- | ---- | ---- | --- | ---- | --- |
 | AI21 Labs | Jamba Instruct | February 1, 2025 | February 1, 2025 | March 1, 2025 | [AI21-Jamba-1.5-Large](https://ai.azure.com/explore/models/AI21-Jamba-1.5-Large/version/1/registry/azureml-ai21) or [AI21-Jamba-1.5-Mini](https://ai.azure.com/explore/models/AI21-Jamba-1.5-Mini/version/1/registry/azureml-staging) |
+| Cohere | [Command R](https://aka.ms/azureai/landing/Cohere-command-r) | February 24, 2025 | March 25, 2025 | June 30, 2025 | [Cohere Command R 08-2024](https://aka.ms/azureai/landing/Cohere-command-r-08-2024) |
+| Cohere | [Command R+](https://aka.ms/azureai/landing/Cohere-command-r-plus) | February 24, 2025 | March 25, 2025 | June 30, 2025 | [Cohere Command R+ 08-2024](https://aka.ms/azureai/landing/Cohere-command-r-plus-08-2024) |
 | Mistral AI | [Mistral-large-2407](https://aka.ms/azureai/landing/Mistral-Large-2407) | January 13, 2025 | February 13, 2025 | May 13, 2025 | [Mistral-large-2411](https://aka.ms/aistudio/landing/Mistral-Large-2411) |
 | Mistral AI | [Mistral-large](https://aka.ms/azureai/landing/Mistral-Large) | December 15, 2024 | January 15, 2025 | April 15, 2025 | [Mistral-large-2411](https://aka.ms/aistudio/landing/Mistral-Large-2411) |
 

@@ -1,23 +1,26 @@
 ---
-title: How to configure a managed network for Azure AI Foundry hubs
+title: How to configure a managed network
 titleSuffix: Azure AI Foundry
-description: Learn how to configure a managed network for Azure AI Foundry hubs.
+description: Learn how to configure a managed network for Azure AI Foundry hubs. A managed network secures your computing resources.
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.custom: ignite-2023, build-2024, devx-track-azurecli, ignite-2024
 ms.topic: how-to
-ms.date: 11/19/2024
+ms.date: 02/24/2025
 ms.reviewer: meerakurup
 ms.author: larryfr
 author: Blackmist
 zone_pivot_groups: azure-ai-studio-sdk-cli
+
+#Customer intent: As an administrator, I want to configure a managed network for Azure AI Foundry hubs so that my computing resources are protected.
+
 ---
 
 # How to configure a managed network for Azure AI Foundry hubs
 
 We have two network isolation aspects. One is the network isolation to access an [Azure AI Foundry](https://ai.azure.com) hub. Another is the network isolation of computing resources for both your hub and project (such as compute instance, serverless and managed online endpoint.) This document explains the latter highlighted in the diagram. You can use hub built-in network isolation to protect your computing resources.
 
-:::image type="content" source="../media/how-to/network/azure-ai-network-outbound.svg" alt-text="Diagram of hub network isolation." lightbox="../media/how-to/network/azure-ai-network-outbound.png":::
+:::image type="content" source="../media/how-to/network/azure-ai-network-outbound.svg" alt-text="Diagram of hub network isolation configuration with Azure AI Foundry." lightbox="../media/how-to/network/azure-ai-network-outbound.png":::
 
 You need to configure following network isolation configurations.
 
@@ -54,10 +57,13 @@ The following diagram shows a managed virtual network configured to __allow inte
 The following diagram shows a managed virtual network configured to __allow only approved outbound__:
 
 > [!NOTE]
-> In this configuration, the storage, key vault, and container registry used by the hub are flagged as private. Since they are flagged as private, a private endpoint is used to communicate with them.
+> In this configuration, the storage, key vault, and container registry used by the hub are flagged as private. Since they're flagged as private, a private endpoint is used to communicate with them.
 
 :::image type="content" source="../media/how-to/network/only-approved-outbound.svg" alt-text="Diagram of managed virtual network isolation configured for allow only approved outbound." lightbox="../media/how-to/network/only-approved-outbound.png":::
 
+> [!NOTE]
+> When you're accessing a private storage account from a public AI Foundry hub, you must access AI Foundry from within the virtual network of your storage account. Accessing AI Foundry from within the virtual network ensures that you can call actions such as upload files to the private storage account. The private storage account is independent of your AI Foundry hub's networking settings. For more on setting your private storage accounts virtual network, see [Configure Azure Storage firewalls and virtual networks](/azure/storage/common/storage-network-security).
+
 ## Prerequisites
 
 Before following the steps in this article, make sure you have the following prerequisites:
@@ -135,7 +141,7 @@ Before following the steps in this article, make sure you have the following pre
 
 ## Limitations
 
-* Azure AI Foundry supports managed virtual network isolation for securing your compute resources. Azure AI Foundry does not support bring your own virtual network for securing compute resources. Please note bring your own virtual network for securing computes is different than your Azure virtual network that is required to access Azure AI Foundry from your on-premises network. 
+* Azure AI Foundry supports managed virtual network isolation for securing your compute resources. Azure AI Foundry doesn't support bring your own virtual network for securing compute resources. Note that bringing your own virtual network for securing computes is different than your Azure virtual network that is required to access Azure AI Foundry from your on-premises network. 
 * Once you enable managed virtual network isolation of your Azure AI, you can't disable it.
 * Managed virtual network uses private endpoint connection to access your private resources. You can't have a private endpoint and a service endpoint at the same time for your Azure resources, such as a storage account. We recommend using private endpoints in all scenarios.
 * The managed virtual network is deleted when the Azure AI is deleted. 
@@ -149,14 +155,14 @@ Before following the steps in this article, make sure you have the following pre
 ## Configure a managed virtual network to allow internet outbound
 
 > [!TIP]
-> The creation of the managed VNet is deferred until a compute resource is created or provisioning is manually started. When allowing automatic creation, it can take around __30 minutes__ to create the first compute resource as it is also provisioning the network.
+> The creation of the managed virtual network is deferred until a compute resource is created or provisioning is manually started. When allowing automatic creation, it can take around __30 minutes__ to create the first compute resource as it is also provisioning the network.
 
 # [Azure portal](#tab/portal)
 
 * __Create a new hub__:
 
     1. Sign in to the [Azure portal](https://portal.azure.com), and choose Azure AI Foundry from Create a resource menu.
-    1. Select **+ New Azure AI**.
+    1. Select __+ New Azure AI__.
     1. Provide the required information on the __Basics__ tab.
     1. From the __Networking__ tab, select __Private with Internet Outbound__.
     1. To add an _outbound rule_, select __Add user-defined outbound rules__ from the __Networking__ tab. From the __Outbound rules__ sidebar, provide the following information:
@@ -332,14 +338,14 @@ To configure a managed virtual network that allows internet outbound communicati
 ## Configure a managed virtual network to allow only approved outbound
 
 > [!TIP]
-> The managed VNet is automatically provisioned when you create a compute resource. When allowing automatic creation, it can take around __30 minutes__ to create the first compute resource as it is also provisioning the network. If you configured FQDN outbound rules, the first FQDN rule adds around __10 minutes__ to the provisioning time.
+> The managed virtual network is automatically provisioned when you create a compute resource. When allowing automatic creation, it can take around __30 minutes__ to create the first compute resource as it is also provisioning the network. If you configured FQDN outbound rules, the first FQDN rule adds around __10 minutes__ to the provisioning time.
 
 # [Azure portal](#tab/portal)
 
 * __Create a new hub__:
 
     1. Sign in to the [Azure portal](https://portal.azure.com), and choose Azure AI Foundry from Create a resource menu.
-    1. Select **+ New Azure AI**.
+    1. Select __+ New Azure AI__.
     1. Provide the required information on the __Basics__ tab.
     1. From the __Networking__ tab, select __Private with Approved Outbound__.
 
@@ -624,7 +630,7 @@ To reduce the wait time and avoid potential timeout errors, we recommend manuall
 Alternatively, you can use the `provision_network_now` flag to provision the managed network as part of hub creation. This flag is in preview.
 
 > [!NOTE]
-> To create an online deployment, you must manually provision the managed network, or create a compute instance first which will automatically provision it. 
+> To create an online deployment, you must manually provision the managed network, or create a compute instance first. Creating a compute instance automatically provision it. 
 
 # [Azure portal](#tab/portal)
 
@@ -644,7 +650,7 @@ The following example shows how to provision a managed virtual network.
 az ml workspace provision-network -g my_resource_group -n my_ai_hub_name
 ```
 
-To verify that the provisioning has completed, use the following command:
+To verify that the provisioning completed, use the following command:
 
 ```azurecli
 az ml workspace show -n my_ai_hub_name -g my_resource_group --query managed_network
@@ -667,7 +673,7 @@ ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group,
 provision_network_result = ml_client.workspaces.begin_provision_network(workspace_name=ai_hub_name).result()
 ```
 
-To verify that the AI Hub has been provisioned, use `ml_client.workspaces.get()` to get the AI Hub information. The `managed_network` property contains the status of the managed network.
+To verify the AI provisioning, use `ml_client.workspaces.get()` to get the AI Hub information. The `managed_network` property contains the status of the managed network.
 
 ```python
 ws = ml_client.workspaces.get()
@@ -761,7 +767,7 @@ __Inbound__ service tag rules:
 To allow installation of __Python packages for training and deployment__, add outbound _FQDN_ rules to allow traffic to the following host names:
 
 > [!NOTE]
-> This is not a complete list of the hosts required for all Python resources on the internet, only the most commonly used. For example, if you need access to a GitHub repository or other host, you must identify and add the required hosts for that scenario.
+> This isn't a complete list of the hosts required for all Python resources on the internet, only the most commonly used. For example, if you need access to a GitHub repository or other host, you must identify and add the required hosts for that scenario.
 
 | __Host name__ | __Purpose__ |
 | ---- | ---- |
@@ -779,7 +785,7 @@ Visual Studio Code relies on specific hosts and ports to establish a remote conn
 The hosts in this section are used to install Visual Studio Code packages to establish a remote connection between Visual Studio Code and the compute instances for your project.
 
 > [!NOTE]
-> This is not a complete list of the hosts required for all Visual Studio Code resources on the internet, only the most commonly used. For example, if you need access to a GitHub repository or other host, you must identify and add the required hosts for that scenario. For a complete list of host names, see [Network Connections in Visual Studio Code](https://code.visualstudio.com/docs/setup/network).
+> This isn't a complete list of the hosts required for all Visual Studio Code resources on the internet, only the most commonly used. For example, if you need access to a GitHub repository or other host, you must identify and add the required hosts for that scenario. For a complete list of host names, see [Network Connections in Visual Studio Code](https://code.visualstudio.com/docs/setup/network).
 
 | __Host name__ | __Purpose__ |
 | ---- | ---- |
@@ -847,7 +853,7 @@ When you create a private endpoint for hub dependency resources, such as Azure S
 A private endpoint is automatically created for a connection if the target resource is an Azure resource listed previously. A valid target ID is expected for the private endpoint. A valid target ID for the connection can be the Azure Resource Manager ID of a parent resource. The target ID is also expected in the target of the connection or in `metadata.resourceid`. For more on connections, see [How to add a new connection in Azure AI Foundry portal](connections-add.md).
 
 > [!IMPORTANT]
-> As of March 31st 2025, the Azure AI Enterprise Network Connection Approver role must be assigned to the Azure AI Foundry hub's managed identity to approve private endpoints to securely access your Azure resources from the managed virtual network. This does not impact existing resources with approved private endpoints as the role is correctly assigned by the service. For new resources, please ensure the role is assigned to the hub's managed identity. For Azure Data Factory, Azure Databricks, and Azure Function Apps, the Contributor role should instead be assigned to your hub's managed identity. This role assignment is applicable to both User-assigned identity and System-assigned identity workspaces. 
+> As of March 31st 2025, the Azure AI Enterprise Network Connection Approver role must be assigned to the Azure AI Foundry hub's managed identity to approve private endpoints to securely access your Azure resources from the managed virtual network. This doesn't impact existing resources with approved private endpoints as the role is correctly assigned by the service. For new resources, please ensure the role is assigned to the hub's managed identity. For Azure Data Factory, Azure Databricks, and Azure Function Apps, the Contributor role should instead be assigned to your hub's managed identity. This role assignment is applicable to both User-assigned identity and System-assigned identity workspaces. 
 
 ## Select an Azure Firewall version for allowed only approved outbound (Preview)
 
@@ -899,7 +905,7 @@ The hub managed virtual network feature is free. However, you're charged for the
 * FQDN outbound rules - FQDN outbound rules are implemented using Azure Firewall. If you use outbound FQDN rules, charges for Azure Firewall are included in your billing. A standard version of Azure Firewall is used by default. For information on selecting the basic version, see [Select an Azure Firewall version](#select-an-azure-firewall-version-for-allowed-only-approved-outbound-preview). Azure Firewall is provisioned per hub.
 
     > [!IMPORTANT]
-    > The firewall isn't created until you add an outbound FQDN rule. If you don't use FQDN rules, you will not be charged for Azure Firewall. For more information on pricing, see [Azure Firewall pricing](https://azure.microsoft.com/pricing/details/azure-firewall/).
+    > The firewall isn't created until you add an outbound FQDN rule. If you don't use FQDN rules, you won't be charged for Azure Firewall. For more information on pricing, see [Azure Firewall pricing](https://azure.microsoft.com/pricing/details/azure-firewall/).
 
 ## Related content
 

@@ -154,6 +154,32 @@ The supported file type is JSON Lines. Files are uploaded to the default datasto
 
 Once your model is fine-tuned, you can deploy it and use it in your own application, in the playground, or in prompt flow. For more information on how to use deployed models, see [How to use Mistral premium chat models](./deploy-models-mistral.md).
 
+---
+## Supported enterprise scenarios for finetuning
+
+Several enterprise scenarios are supported for MaaS finetuning. The table below outlines the supported configurations for user storage networking and authentication to ensure smooth operation within enterprise scenarios:
+
+>[!Note]  
+>- Data connections auth can be changed via AI Studio by clicking on the datastore connection which your dataset is stored in, and navigating to the **Access details** > **Authentication Method** setting.  
+>- Storage auth can be changed in Azure Storage > **Settings** > **Configurations** page > **Allow storage account key access**.  
+>- Storage networking can be changed in Azure Storage > **Networking** page.
+
+| **Storage Networking**                                       |  **Storage Auth**               | **Data Connection Auth**               | **Support**           |
+| ------------------------------------------------------------ | ------------------------------ | --------------------------------- | ----------------------- |
+| Public Network Access = Enabled                               | Account key enabled            | SAS/Account Key                  | Yes, UX and SDK         |
+| Public Network Access = Enabled                               | Account key disabled           | Entra-Based Auth (Credentialless) | Yes, UX and SDK <br><br> *Note:* for UX, you may need to add Storage Blob Data Reader or Storage Blob Data Contributor for your user ID on the storage account, or change the connection's authentication to use Account key/SAS token |                               |                                   |                         |
+| Enabled from selected virtual networks and IP addresses      | Account key enabled            | Account key                      | Yes, UX and SDK <br><br> *Note:*: for UX, the IP of the compute running the browser must be in the selected list        |
+| Enabled from selected virtual networks and IP addresses      | Account key enabled            | SAS                               | Yes, UX and SDK  <br><br> *Note:*: for UX, the IP of the compute running the browser must be in the selected list       |
+| Enabled from selected virtual networks and IP addresses      | Account key disabled           | Entra-Based Auth (Credentialless) | Yes, UX and SDK. <br><br>*Note:* for UX, you may need to add Storage Blob Data Reader or Storage Blob Data Contributor for your user ID on the storage account, or change the connection's authentication to use Account key/SAS token. Also ensure the IP of the compute running the browser must be in the selected list |                               |                                   |                         |
+| Public Network Access = Disabled                              | Account key enabled            | SAS/Account Key                  | Yes, UX and SDK. <br><br> *Note:*  for UX data upload and submission to work, the workspace _needs to be accessed from within the Vnet_ that has appropriate access to the storage           |
+| Public Network Access = Disabled                              | Account key disabled           | Entra-Based Auth (Credentialless) | Yes, UX and SDK. <br><br> *Note:* for UX data upload and submission to work, the workspace _needs to be accessed from within the Vnet_ that has appropriate access to the storage                |
+
+
+The scenarios above should work in a Managed Vnet workspace as well. See setup of Managed Vnet AI Studio hub here: [How to configure a managed network for Azure AI Foundry hubs](./configure-managed-network.md)
+
+Customer-Managed Keys (CMKs) is **not** a supported enterprise scenario with MaaS finetuning.
+
+Issues finetuning with unique network setups on the workspace and storage usually points to a networking setup issue.
 
 ---
 

@@ -13,6 +13,7 @@ ms.custom: include, ignite-2024
 You install the Azure CLI and sign in from your local development environment, so that you can use your user credentials to call the Azure OpenAI service.
 
 In most cases you can install the Azure CLI from your terminal using the following command: 
+
 # [Windows](#tab/windows)
 
 ```powershell 
@@ -36,9 +37,11 @@ brew update && brew install azure-cli
 You can follow instructions [How to install the Azure CLI](/cli/azure/install-azure-cli) if these commands don't work for your particular operating system or setup.
 
 After you install the Azure CLI, sign in using the ``az login`` command and sign-in using the browser:
+
 ```
 az login
 ```
+
 Alternatively, you can log in manually via the browser with a device code.
 ```
 az login --use-device-code

@@ -22,12 +22,10 @@ In this quickstart, we walk you through setting up your local development enviro
 
 * Before you can follow this quickstart, complete the [Azure AI Foundry playground quickstart](../quickstarts/get-started-playground.md) to deploy a **gpt-4o-mini** model into a project.
 
-## Install the Azure CLI and sign in 
-
-[!INCLUDE [Install the Azure CLI](../includes/install-cli.md)]
-
 ## Create a new Python environment
 
+In the IDE of your choice, create a new folder for your project.  Open a terminal window in that folder.
+
 [!INCLUDE [Install Python](../includes/install-python.md)]
 
 ## Install packages
@@ -38,6 +36,12 @@ Install `azure-ai-projects`(preview), `azure-ai-inference` (preview), and azure-
 pip install azure-ai-projects azure-ai-inference azure-identity 
 ```
 
+## Install the Azure CLI and sign in 
+
+[!INCLUDE [Install the Azure CLI](../includes/install-cli.md)]
+
+Keep this terminal window open to run your python scripts from here as well, now that you've signed in.
+
 ## Build your chat app
 
 Create a file named **chat.py**.  Copy and paste the following code into it.

@@ -384,7 +384,9 @@ items:
     items:
     - name: Fine-tuning in Azure AI Foundry
       href: concepts/fine-tuning-overview.md
-    - name: Fine-tune with a managed compute
+    - name: Fine-tune models deployed via serverless API
+      href: how-to/fine-tune-serverless.md
+    - name: Fine-tune models deployed via managed compute
       href: how-to/fine-tune-managed-compute.md
     - name: Fine-tune Azure OpenAI models
       href: ../ai-services/openai/how-to/fine-tuning.md?context=/azure/ai-studio/context/context

@@ -110,12 +110,11 @@ In the Azure AI Foundry portal, check for an Azure AI Search connected resource.
 
 1. Select **Add connection**.  
 
-## <a name="installs"></a> Install the Azure CLI and sign in 
-
-[!INCLUDE [Install the Azure CLI](../includes/install-cli.md)]
 
 ## Create a new Python environment
 
+In the IDE of your choice, create a new folder for your project.  Open a terminal window in that folder.
+
 [!INCLUDE [Install Python](../includes/install-python.md)]
 
 ## Install packages
@@ -145,6 +144,12 @@ Create a folder for your work. Create a file called **config.py** in this folder
 
 [!INCLUDE [create-env-file](../includes/create-env-file-tutorial.md)]
 
+## <a name="installs"></a> Install the Azure CLI and sign in 
+
+[!INCLUDE [Install the Azure CLI](../includes/install-cli.md)]
+
+Keep this terminal window open to run your python scripts from here as well, now that you've signed in.
+
 ## Clean up resources
 
 To avoid incurring unnecessary Azure costs, you should delete the resources you created in this tutorial if they're no longer needed. To manage resources, you can use the [Azure portal](https://portal.azure.com?azure-portal=true).