Merge branch 'MicrosoftDocs:main' into CR

Author: tejaswikolli-web

.openpublishing.redirection.azure-monitor.json

@@ -10,6 +10,11 @@
             "redirect_url": "/azure/azure-monitor/vm/scom-managed-instance-overview",
             "redirect_document_id": false
          },
+        {
+            "source_path_from_root": "/articles/azure-monitor/scom-manage-instance/monitor-arc-enabled-vm-with-scom-managed-instance.md",
+            "redirect_url": "/azure/azure-monitor/scom-manage-instance/monitor-azure-off-azure-vm-with-scom-managed-instance",
+            "redirect_document_id": false
+         },
         {
             "source_path_from_root": "/articles/azure-monitor/scom-manage-instance/common-questions.md",
             "redirect_url": "/azure/azure-monitor/scom-manage-instance/scom-managed-instance-faq",

.openpublishing.redirection.json

@@ -1,5 +1,10 @@
 {
     "redirections": [
+    {
+      "source_path": "articles/digital-twins/how-to-integrate-maps.md",
+      "redirect_url": "/previous-versions/azure/digital-twins/how-to-integrate-maps",
+      "redirect_document_id": false
+    },
     {
       "source_path": "articles/mysql/single-server/azure-pipelines-mysql-deploy.md",
       "redirect_url": "/previous-versions/azure/mysql/single-server/azure-pipelines-mysql-deploy",

articles/ai-services/document-intelligence/concept-accuracy-confidence.md

@@ -8,7 +8,7 @@ ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 07/09/2024
+ms.date: 07/11/2024
 ms.author: lajanuar
 ---
 

articles/ai-services/document-intelligence/create-sas-tokens.md

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
-ms.date: 07/18/2023
+ms.date: 07/11/2024
 ms.author: lajanuar
 ---
 
@@ -24,11 +24,9 @@ ms.author: lajanuar
 
 At a high level, here's how SAS tokens work:
 
-* Your application submits the SAS token to Azure Storage as part of a REST API request.
+* First, your application submits the SAS token to Azure Storage as part of a REST API request.
 
-* If the storage service verifies that the SAS is valid, the request is authorized.
-
-* If the SAS token is deemed invalid, the request is declined and the error code 403 (Forbidden) is returned.
+* Next, if the storage service verifies that the SAS is valid, the request is authorized. If, the SAS token is deemed invalid, the request is declined and the error code 403 (Forbidden) is returned.
 
 Azure Blob Storage offers three resource types:
 
@@ -109,26 +107,26 @@ The Azure portal is a web-based console that enables you to manage your Azure su
     >
     >     :::image type="content" source="media/sas-tokens/need-permissions.png" alt-text="Screenshot that shows the lack of permissions warning.":::
     >
-     > * [Azure role-based access control](../../role-based-access-control/overview.md) (Azure RBAC) is the authorization system used to manage access to Azure resources. Azure RBAC helps you manage access and permissions for your Azure resources.
+    > * [Azure role-based access control](../../role-based-access-control/overview.md) (Azure RBAC) is the authorization system used to manage access to Azure resources. Azure RBAC helps you manage access and permissions for your Azure resources.
     > * [Assign an Azure role for access to blob data](../../role-based-access-control/role-assignments-portal.yml?tabs=current) to assign a role that allows for read, write, and delete permissions for your Azure storage container. *See* [Storage Blob Data Contributor](../../role-based-access-control/built-in-roles.md#storage-blob-data-contributor).
 
 1. Specify the signed key **Start** and **Expiry** times.
 
     * When you create a SAS token, the default duration is 48 hours. After 48 hours, you'll need to create a new token.
     * Consider setting a longer duration period for the time you're using your storage account for Document Intelligence Service operations.
     * The value of the expiry time is determined by whether you're using an **Account key** or **User delegation key** **Signing method**:
-      * **Account key**: There's no imposed maximum time limit; however, best practices recommended that you configure an expiration policy to limit the interval and minimize compromise. [Configure an expiration policy for shared access signatures](/azure/storage/common/sas-expiration-policy).
-      * **User delegation key**: The value for the expiry time is a maximum of seven days from the creation of the SAS token. The SAS is invalid after the user delegation key expires, so a SAS with an expiry time of greater than seven days will still only be valid for seven days. For more information,*see* [Use Microsoft Entra credentials to secure a SAS](/azure/storage/blobs/storage-blob-user-delegation-sas-create-cli#use-azure-ad-credentials-to-secure-a-sas).
+      * **Account key**: No imposed maximum time limit; however, best practices recommended that you configure an expiration policy to limit the interval and minimize compromise. [Configure an expiration policy for shared access signatures](/azure/storage/common/sas-expiration-policy).
+      * **User delegation key**: The value for the expiry time is a maximum of seven days from the creation of the SAS token. The SAS is invalid after the user delegation key expires, so a SAS with an expiry time of greater than seven days will still only be valid for seven days. For more information, *see* [Use Microsoft Entra credentials to secure a SAS](/azure/storage/blobs/storage-blob-user-delegation-sas-create-cli#use-azure-ad-credentials-to-secure-a-sas).
 
-1. The **Allowed IP addresses** field is optional and specifies an IP address or a range of IP addresses from which to accept requests. If the request IP address doesn't match the IP address or address range specified on the SAS token, authorization fails. The IP address or a range of IP addresses must be public IPs, not private. For more information,*see*, [**Specify an IP address or IP range**](/rest/api/storageservices/create-account-sas#specify-an-ip-address-or-ip-range).
+1. The **Allowed IP addresses** field is optional and specifies an IP address or a range of IP addresses from which to accept requests. If the request IP address doesn't match the IP address or address range specified on the SAS token, authorization fails. The IP address or a range of IP addresses must be public IPs, not private. For more information, *see*, [**Specify an IP address or IP range**](/rest/api/storageservices/create-account-sas#specify-an-ip-address-or-ip-range).
 
 1. The **Allowed protocols** field is optional and specifies the protocol permitted for a request made with the SAS token. The default value is HTTPS.
 
 1. Select **Generate SAS token and URL**.
 
 1. The **Blob SAS token** query string and **Blob SAS URL** appear in the lower area of the window. To use the Blob SAS token, append it to a storage service URI.
 
-1. Copy and paste the **Blob SAS token** and **Blob SAS URL** values in a secure location. They're displayed only once and can't be retrieved after the window is closed.
+1. Copy and paste the **Blob SAS token** and **Blob SAS URL** values in a secure location. The values are displayed only once and can't be retrieved after the window is closed.
 
 1. To [construct a SAS URL](#use-your-sas-url-to-grant-access), append the SAS token (URI) to the URL for a storage service.
 
@@ -176,7 +174,7 @@ To use your SAS URL with the [REST API](/rest/api/aiservices/document-models/bui
   }
   ```
 
-That's it! You've learned how to create SAS tokens to authorize how clients access your data.
+That's it! You learned how to create SAS tokens to authorize how clients access your data.
 
 ## Next step
 

articles/ai-services/document-intelligence/deploy-label-tool.md

@@ -8,7 +8,7 @@ ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 07/18/2023
+ms.date: 07/11/2024
 ms.author: lajanuar
 monikerRange: 'doc-intel-2.1.0'
 ---
@@ -32,7 +32,7 @@ The Document Intelligence Sample Labeling tool is an application that provides a
 
 * [Run the Sample Labeling tool locally](#run-the-sample-labeling-tool-locally)
 * [Deploy the Sample Labeling tool to an Azure Container Instance (ACI)](#deploy-with-azure-container-instances-aci)
-* [Use and contribute to the open-source OCR Form Labeling Tool](#open-source-on-github)
+* [Use and contribute to the open-source Form Labeling Tool](#open-source-on-github)
 
 ## Run the Sample Labeling tool locally
 
@@ -97,7 +97,7 @@ Follow these steps to create a new resource using the Azure portal:
 
 ### Continuous deployment
 
-After you've created your web app, you can enable the continuous deployment option:
+After you create your web app, you can enable the continuous deployment option:
 
 * From the left pane, choose **Container settings**.
 * In the main window, navigate to Continuous deployment and toggle between the **On** and **Off** buttons to set your preference:
@@ -116,10 +116,10 @@ As an alternative to using the Azure portal, you can create a resource using the
 
 There's a few things you need know about this command:
 
-* `DNS_NAME_LABEL=aci-demo-$RANDOM` generates a random DNS name.
+* `DNS_NAME_LABEL=aci-demo-$RANDOM` generates a random Domain Name System (DNS) identity.
 * This sample assumes that you have a resource group that you can use to create a resource. Replace `<resource_group_name>` with a valid resource group associated with your subscription.
 * You need to specify where you want to create the resource. Replace `<region name>` with your desired region for the web app.
-* This command automatically accepts EULA.
+* This command automatically accepts End User License Agreement (EULA).
 
 From the Azure CLI, run this command to create a web app resource for the Sample Labeling tool:
 
@@ -145,11 +145,11 @@ az container create \
 
 ### Connect to Microsoft Entra ID for authorization
 
-It's recommended that you connect your web app to Microsoft Entra ID. This connection ensures that only users with valid credentials can sign in and use your web app. Follow the instructions in [Configure your App Service app](../../app-service/configure-authentication-provider-aad.md) to connect to Microsoft Entra ID.
+We recommend that you connect your web app to Microsoft Entra ID. This connection ensures that only users with valid credentials can sign in and use your web app. Follow the instructions in [Configure your App Service app](../../app-service/configure-authentication-provider-aad.md) to connect to Microsoft Entra ID.
 
 ## Open source on GitHub
 
-The OCR Form Labeling Tool is also available as an open-source project on GitHub. The tool is a web application built using React + Redux, and is written in TypeScript. To learn more or contribute, see [OCR Form Labeling Tool](https://github.com/microsoft/OCR-Form-Tools/blob/master/README.md).
+The Form Labeling Tool is also available as an open-source project on GitHub. The tool is a web application built using React + Redux, and is written in TypeScript. To learn more or contribute, see [Form Labeling Tool](https://github.com/microsoft/OCR-Form-Tools/blob/master/README.md).
 
 ## Next steps
 

articles/ai-services/document-intelligence/label-tool.md

@@ -1,14 +1,14 @@
 ---
 title: "How-to: Analyze documents, Label forms, train a model, and analyze forms with Document Intelligence (formerly Form Recognizer)"
 titleSuffix: Azure AI services
-description: How to use the Document Intelligence sample tool to analyze documents, invoices, receipts etc. Label and create a custom model to extract text, tables, selection marks, structure and key-value pairs from documents.
+description: How to use the Document Intelligence sample tool to analyze documents, invoices, receipts etc. Label and create a custom model to extract text, tables, selection marks, structure, and key-value pairs from documents.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 07/18/2023
+ms.date: 07/11/2024
 ms.author: lajanuar
 monikerRange: 'doc-intel-2.1.0'
 ---
@@ -158,7 +158,7 @@ When you create or open a project, the main tag editor window opens. The tag edi
 
 Select **Run Layout on unvisited documents** on the left pane to get the text and table layout information for each document. The labeling tool draws bounding boxes around each text element.
 
-The labeling tool also shows which tables have been automatically extracted. Select the table/grid icon on the left hand of the document to see the extracted table. In this quickstart, because the table content is automatically extracted, we don't label the table content, but rather rely on the automated extraction.
+The labeling tool also shows which tables were automatically extracted. To see extracted tables, select the table/grid icon on the left hand of the document. In this quickstart, because the table content is automatically extracted, we don't label the table content, but rather rely on the automated extraction.
 
 :::image type="content" source="media/label-tool/table-extraction.png" alt-text="Table visualization in Sample Labeling tool.":::
 
@@ -249,13 +249,13 @@ At times, your data might lend itself better to being labeled as a table rather
 
 :::image type="content" source="media/label-tool/table-tag.png" alt-text="Configuring a table tag.":::
 
-Once you've defined your table tag, tag the cell values.
+Once you define your table tag, tag the cell values.
 
 :::image type="content" source="media/table-labeling.png" alt-text="Labeling a table.":::
 
 ## Train a custom model
 
-Choose the Train icon on the left pane to open the Training page. Then select the **Train** button to begin training the model. Once the training process completes, you see the following information:
+To open the Training page, choose the Train icon on the left pane. Then select the **Train** button to begin training the model. Once the training process completes, you see the following information:
 
 * **Model ID** - The ID of the model that was created and trained. Each training call creates a new model with its own ID. Copy this string to a secure location; you need it if you want to do prediction calls through the [REST API](~/articles/ai-services/document-intelligence/how-to-guides/use-sdk-rest-api.md?view=doc-intel-2.1.0&preserve-view=true?pivots=programming-language-rest-api&tabs=preview%2cv2-1) or [client library guide](~/articles/ai-services/document-intelligence/how-to-guides/use-sdk-rest-api.md?view=doc-intel-2.1.0&preserve-view=true).
 * **Average Accuracy** - The model's average accuracy. You can improve model accuracy by adding and labeling more forms, then retraining to create a new model. We recommend starting by labeling five forms and adding more forms as needed.
@@ -264,7 +264,7 @@ Choose the Train icon on the left pane to open the Training page. Then select th
 
 :::image type="content" source="media/label-tool/train-screen.png" alt-text="Training view.":::
 
-After training finishes, examine the **Average Accuracy** value. If it's low, you should add more input documents and repeat the labeling steps. The documents you've already labeled remain in the project index.
+After training finishes, examine the **Average Accuracy** value. If it's low, you should add more input documents and repeat the labeling steps. The documents you already labeled remain in the project index.
 
 > [!TIP]
 > You can also run the training process with a REST API call. To learn how to do this, see [Train with labels using Python](https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/python/FormRecognizer/rest/python-labeled-data.md).
@@ -282,16 +282,16 @@ With Model Compose, you can compose up to 200 models to a single model ID. When
 
 ## Analyze a form
 
-Select the Analyze icon from the navigation bar to test your model. Select source *Local file*. Browse for a file and select a file from the sample dataset that you unzipped in the test folder. Then choose the **Run analysis** button to get key/value pairs, text and tables predictions for the form. The tool applies tags in bounding boxes and reports the confidence of each tag.
+To test your model, select the `Analyze` icon from the navigation bar. Select source *Local file*. Browse for a file and select a file from the sample dataset that you unzipped in the test folder. Then choose the **Run analysis** button to get key/value pairs, text, and tables predictions for the form. The tool applies tags in bounding boxes and reports the confidence of each tag.
 
 :::image type="content" source="media/analyze.png" alt-text="Screenshot of analyze-a-custom-form window":::
 
 > [!TIP]
-> You can also run the Analyze API with a REST call. To learn how to do this, see [Train with labels using Python](https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/python/FormRecognizer/rest/python-labeled-data.md).
+> You can also run the `Analyze` API with a REST call. To learn how to do this, see [Train with labels using Python](https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/python/FormRecognizer/rest/python-labeled-data.md).
 
 ## Improve results
 
-Depending on the reported accuracy, you may want to do further training to improve the model. After you've done a prediction, examine the confidence values for each of the applied tags. If the average accuracy training value is high, but the confidence scores are low (or the results are inaccurate), add the prediction file to the training set, label it, and train again.
+Depending on the reported accuracy, you may want to do further training to improve the model. After you complete a prediction, examine the confidence values for each of the applied tags. If the average accuracy training value is high, but the confidence scores are low (or the results are inaccurate), add the prediction file to the training set, label it, and train again.
 
 The reported average accuracy, confidence scores, and actual accuracy can be inconsistent when the analyzed documents differ from documents used in training. Keep in mind that some documents look similar when viewed by people but can look distinct to the AI model. For example, you might train with a form type that has two variations, where the training set consists of 20% variation A and 80% variation B. During prediction, the confidence scores for documents of variation A are likely to be lower.
 
@@ -313,7 +313,7 @@ Finally, go to the main page (house icon) and select **Open Cloud Project**. The
 
 ## Next steps
 
-In this quickstart, you've learned how to use the Document Intelligence Sample Labeling tool to train a model with manually labeled data. If you'd like to build your own utility to label training data, use the REST APIs that deal with labeled data training.
+In this quickstart, you learned how to use the Document Intelligence Sample Labeling tool to train a model with manually labeled data. If you'd like to build your own utility to label training data, use the REST APIs that deal with labeled data training.
 
 > [!div class="nextstepaction"]
 > [Train with labels using Python](https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/python/FormRecognizer/rest/python-labeled-data.md)