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Merge pull request #202593 from alexbuckgit/alexbuckgit/fix-links-20220623-02
Fix link formatting (2)
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articles/active-directory/managed-identities-azure-resources/tutorial-vm-managed-identities-cosmos.md

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```
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The user assigned managed identity should be specified using its [resourceID](how-manage-user-assigned-managed-identities.md
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).
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The user assigned managed identity should be specified using its [resourceID](./how-manage-user-assigned-managed-identities.md).
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# [Azure CLI](#tab/azure-cli)
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articles/applied-ai-services/form-recognizer/includes/get-started/javascript.md

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> [!IMPORTANT]
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>
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> Remember to remove the key from your code when you're done, and never post it publicly. For production, use secure methods to store and access your credentials. See our Cognitive Services [security]((/azure/cognitive-services/cognitive-services-security.md) article for more information.
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> Remember to remove the key from your code when you're done, and never post it publicly. For production, use secure methods to store and access your credentials. See our Cognitive Services [security](/azure/cognitive-services/cognitive-services-security.md) article for more information.
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## **Try it**: Layout model
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articles/azure-government/documentation-government-ase-disa-cap.md

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When creating the ASE via the portal, a route table with a default route of 0.0.0.0/0 and next hop “Internet” is created.
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However, since DISA advertises a default route out the ExpressRoute circuit, the User Defined Route (UDR) should either be deleted, or remove the default route to internet.
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You will need to create new routes in the UDR for the management addresses in order to keep the ASE healthy. For Azure Government ranges see [App Service Environment management addresses](../app-service/environment/management-addresses.md
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)
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You will need to create new routes in the UDR for the management addresses in order to keep the ASE healthy. For Azure Government ranges, see [App Service Environment management addresses](../app-service/environment/management-addresses.md).
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- 23.97.29.209/32 --> Internet
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- 13.72.53.37/32 --> Internet

articles/azure-monitor/app/azure-web-apps-net-core.md

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[Trim self-contained deployments](/dotnet/core/deploying/trimming/trim-self-contained) is **not supported**. Use [manual instrumentation](./asp-net-core.md) via code instead.
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See the [enable monitoring section](#enable-monitoring ) below to begin setting up Application Insights with your App Service resource.
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See the [enable monitoring section](#enable-monitoring) below to begin setting up Application Insights with your App Service resource.
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# [Linux](#tab/Linux)
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[Trim self-contained deployments](/dotnet/core/deploying/trimming/trim-self-contained) is **not supported**. Use [manual instrumentation](./asp-net-core.md) via code instead.
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See the [enable monitoring section](#enable-monitoring ) below to begin setting up Application Insights with your App Service resource.
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See the [enable monitoring section](#enable-monitoring) below to begin setting up Application Insights with your App Service resource.
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---
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articles/azure-monitor/best-practices-cost.md

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### Multi-homing agents
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You should be cautious with any configuration using multi-homed agents where a single virtual machine sends data to multiple workspaces since you may be incurring charges for the same data multiple times. If you do multi-home agents, ensure that you're sending unique data to each workspace.
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You can also collect duplicate data with a single virtual machine running both the Azure Monitor agent and Log Analytics agent, even if they're both sending data to the same workspace. While the agents can coexist, each works independently without any knowledge of the other. You should continue to use the Log Analytics agent until you [migrate to the Azure Monitor agent](logs/../agents/azure-monitor-agent-migration.md) rather than using both together unless you can ensure that each are collecting unique data.
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You can also collect duplicate data with a single virtual machine running both the Azure Monitor agent and Log Analytics agent, even if they're both sending data to the same workspace. While the agents can coexist, each works independently without any knowledge of the other. You should continue to use the Log Analytics agent until you [migrate to the Azure Monitor agent](./agents/azure-monitor-agent-migration.md) rather than using both together unless you can ensure that each are collecting unique data.
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See [Analyze usage in Log Analytics workspace](logs/analyze-usage.md) for guidance on analyzing your collected data to ensure that you aren't collecting duplicate data for the same machine.
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articles/batch/batch-pool-compute-intensive-sizes.md

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To run CUDA applications on a pool of Linux NC nodes, you need to install necessary NVIDIA Tesla GPU drivers from the CUDA Toolkit. The following sample steps create and deploy a custom Ubuntu 16.04 LTS image with the GPU drivers:
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1. Deploy an Azure NC-series VM running Ubuntu 16.04 LTS. For example, create the VM in the US South Central region.
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2. Add the [NVIDIA GPU Drivers extension](../virtual-machines/extensions/hpccompute-gpu-linux.md
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) to the VM by using the Azure portal, a client computer that connects to the Azure subscription, or Azure Cloud Shell. Alternatively, follow the steps to connect to the VM and [install CUDA drivers](../virtual-machines/linux/n-series-driver-setup.md) manually.
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1. Deploy an Azure NC-series VM running Ubuntu 16.04 LTS. For example, create the VM in the US South Central region.
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2. Add the [NVIDIA GPU Drivers extension](../virtual-machines/extensions/hpccompute-gpu-linux.md) to the VM by using the Azure portal, a client computer that connects to the Azure subscription, or Azure Cloud Shell. Alternatively, follow the steps to connect to the VM and [install CUDA drivers](../virtual-machines/linux/n-series-driver-setup.md) manually.
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3. Follow the steps to create an [Azure Compute Gallery image](batch-sig-images.md) for Batch.
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4. Create a Batch account in a region that supports NC VMs.
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5. Using the Batch APIs or Azure portal, create a pool [using the custom image](batch-sig-images.md) and with the desired number of nodes and scale. The following table shows sample pool settings for the image:

articles/cognitive-services/language-service/custom-text-classification/glossary.md

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A class is a user-defined category that indicates the overall classification of the text. Developers label their data with their classes before they pass it to the model for training.
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## F1 score
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The F1 score is a function of Precision and Recall. It's needed when you seek a balance between [precision](#precision) and [recall](#recall].
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The F1 score is a function of Precision and Recall. It's needed when you seek a balance between [precision](#precision) and [recall](#recall).
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## Model
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