Update azure-machine-learning.md

Author: amee-sumo

docs/integrations/microsoft-azure/azure-machine-learning.md

@@ -8,13 +8,13 @@ import useBaseUrl from '@docusaurus/useBaseUrl';
 
 <img src={useBaseUrl('img/integrations/microsoft-azure/azure-machine-learning.png')} alt="Thumbnail icon" width="50"/>
 
-[Azure Machine Learning](https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning) is a cloud service for accelerating and managing the machine learning project lifecycle which includes training, deploying, and monitoring models. This integration helps in monitoring the training runs, model deployments, and resource utilization of your nodes present in your workspace.
+[Azure Machine Learning](https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning) is a cloud service for accelerating and managing the machine learning project lifecycle that includes training, deploying, and monitoring models. This integration helps in monitoring the training runs, model deployments, and resource utilization of the nodes present in your workspace.
 
 ## Log and metric types
 
 For Azure Machine Learning, you can collect the following logs and metrics:
 
-* **Resource logs**. These logs contains information about clusters, nodes, jobs, deployments, models, and pipeline events. To learn more about the different [log category types](https://learn.microsoft.com/en-us/azure/machine-learning/monitor-azure-machine-learning?view=azureml-api-2#analyzing-logs) and [schemas](https://learn.microsoft.com/en-us/azure/machine-learning/monitor-resource-reference?view=azureml-api-2#schemas) collected for Azure Machine Learning, refer to the documentation.
+* **Resource logs**. These logs contain information about clusters, nodes, jobs, deployments, models, and pipeline events. To learn more about the different [log category types](https://learn.microsoft.com/en-us/azure/machine-learning/monitor-azure-machine-learning?view=azureml-api-2#analyzing-logs) and [schemas](https://learn.microsoft.com/en-us/azure/machine-learning/monitor-resource-reference?view=azureml-api-2#schemas) collected for Azure Machine Learning, refer to the documentation.
 * **Platform Metrics for Azure Machine Learning**. These metrics are available in [Microsoft.MachineLearningServices/workspaces](https://learn.microsoft.com/en-us/azure/azure-monitor/reference/supported-metrics/microsoft-machinelearningservices-workspaces-metrics) namespace.
 For more information on supported metrics and dimensions, refer to the [Azure documentation](https://learn.microsoft.com/en-us/azure/machine-learning/monitor-resource-reference?view=azureml-api-2#metrics).
 
@@ -25,31 +25,31 @@ Azure service sends monitoring data to Azure Monitor, which can then [stream dat
 * Logs collection from [Azure Monitor](https://docs.microsoft.com/en-us/azure/monitoring-and-diagnostics/monitoring-get-started) using our [Azure Event Hubs source](/docs/send-data/collect-from-other-data-sources/azure-monitoring/ms-azure-event-hubs-source/).
 * Metrics collection using our [HTTP Logs and Metrics source](/docs/send-data/collect-from-other-data-sources/azure-monitoring/collect-metrics-azure-monitor/) via Azure Functions deployed using the ARM template.
 
-You must explicitly enable diagnostic settings for each machine learning workspace you want to monitor. You can forward logs to the same event hub provided they satisfy the limitations and permissions as described [here](https://learn.microsoft.com/en-us/azure/azure-monitor/essentials/diagnostic-settings?tabs=portal#destination-limitations).
+You must explicitly enable diagnostic settings for each machine-learning workspace you want to monitor. You can forward logs to the same event hub provided they satisfy the limitations and permissions as described [here](https://learn.microsoft.com/en-us/azure/azure-monitor/essentials/diagnostic-settings?tabs=portal#destination-limitations).
 
 When you configure the event hubs source or HTTP source, plan your source category to ease the querying process. A hierarchical approach allows you to make use of wildcards. For example: `Azure/MachineLearning/Logs`, `Azure/MachineLearning/Metrics`.
 
 ### Configure metrics collection
 
-In this section, you will configure a pipeline for shipping metrics from Azure Monitor to an Event Hub, on to an Azure Function, and finally to an HTTP Source on a hosted collector in Sumo Logic.
+In this section, you will configure a pipeline for shipping metrics from Azure Monitor to an Event Hub, onto an Azure Function, and finally to an HTTP Source on a hosted collector in Sumo Logic.
 
 1. Create a hosted collector and tag the `tenant_name` field. You can get the tenant name using the instructions [here](https://learn.microsoft.com/en-us/azure/active-directory-b2c/tenant-management-read-tenant-name#get-your-tenant-name). <br/><img src={useBaseUrl('img/integrations/microsoft-azure/Azure-Storage-Tag-Tenant-Name.png')} alt="Azure Tag Tenant Name" style={{border: '1px solid gray'}} width="500" />
 1. [Configure an HTTP Source](/docs/send-data/collect-from-other-data-sources/azure-monitoring/collect-metrics-azure-monitor/#step-1-configure-an-http-source).
 1. [Configure and deploy the ARM Template](/docs/send-data/collect-from-other-data-sources/azure-monitoring/collect-metrics-azure-monitor/#step-2-configure-azure-resources-using-arm-template).
-1. [Export metrics to Event Hub](/docs/send-data/collect-from-other-data-sources/azure-monitoring/collect-metrics-azure-monitor/#step-3-export-metrics-for-a-particular-resource-to-event-hub). Perform below steps for each machine learning workspace that you want to monitor.
-   * Choose `Stream to an event hub` as destination.
+1. [Export metrics to Event Hub](/docs/send-data/collect-from-other-data-sources/azure-monitoring/collect-metrics-azure-monitor/#step-3-export-metrics-for-a-particular-resource-to-event-hub). Perform the steps below for each machine learning workspace that you want to monitor.
+   * Choose `Stream to an event hub` as the destination.
    * Select `AllMetrics`.
-   * Use the Event hub namespace created by the ARM template in Step 2 above. You can create a new Event hub or use the one created by ARM template. You can use the default policy `RootManageSharedAccessKey` as the policy name.
+   * Use the Event hub namespace created by the ARM template in Step 2 above. You can create a new Event hub or use the one created by the ARM template. You can use the default policy `RootManageSharedAccessKey` as the policy name.
 
 ### Configure logs collection
 
 In this section, you will configure a pipeline for shipping diagnostic logs from Azure Monitor to an Event Hub.
 
 1. To set up the Azure Event Hubs source in Sumo Logic, refer to [Azure Event Hubs Source for Logs](/docs/send-data/collect-from-other-data-sources/azure-monitoring/ms-azure-event-hubs-source/).
-2. To create the Diagnostic settings in Azure portal, refer to the [Azure documentation](https://learn.microsoft.com/en-us/azure/azure-monitor/essentials/diagnostic-settings). Perform below steps for each machine learning workspace that you want to monitor.
+2. To create the Diagnostic settings in the Azure portal, refer to the [Azure documentation](https://learn.microsoft.com/en-us/azure/azure-monitor/essentials/diagnostic-settings). Perform the steps below for each machine learning workspace that you want to monitor.
    * Choose `Stream to an event hub` as the destination.
    * Select `allLogs` or individual log categories.
-   * Use the Event hub namespace and Event hub name configured in previous step in destination details section. You can use the default policy `RootManageSharedAccessKey` as the policy name.
+   * Use the Event hub namespace and Event hub name configured in the previous step in the destination details section. You can use the default policy `RootManageSharedAccessKey` as the policy name.
 
 ## Troubleshooting