Merge pull request #210535 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)

Author: huypub

articles/active-directory/develop/app-objects-and-service-principals.md

@@ -17,7 +17,7 @@ ms.reviewer: sureshja
 
 # Application and service principal objects in Azure Active Directory
 
-This article describes application registration, application objects, and service principals in Azure Active Directory (Azure AD): what they're, how they're used, and how they're related to each other. A multi-tenant example scenario is also presented to illustrate the relationship between an application's application object and corresponding service principal objects.
+This article describes application registration, application objects, and service principals in Azure Active Directory (Azure AD): what they are, how they're used, and how they're related to each other. A multi-tenant example scenario is also presented to illustrate the relationship between an application's application object and corresponding service principal objects.
 
 ## Application registration
 

articles/app-service/overview-diagnostics.md

@@ -22,7 +22,7 @@ To access App Service diagnostics, navigate to your App Service web app or App S
 
 For Azure Functions, navigate to your function app, and in the top navigation, click on **Platform features**, and select **Diagnose and solve problems** from the **Resource management** section.
 
-In the App Service diagnostics homepage, you can peform a search for a symptom with your app, or choose a diagnostic category that best describes the issue with your app. Next, there is a new feature called Risk Alerts that provides an actionable report to improve your App.  Finally, this page is where you can find **Diagnostic Tools**. See [Diagnostic tools](#diagnostic-tools).
+In the App Service diagnostics homepage, you can perform a search for a symptom with your app, or choose a diagnostic category that best describes the issue with your app. Next, there is a new feature called Risk Alerts that provides an actionable report to improve your App.  Finally, this page is where you can find **Diagnostic Tools**. See [Diagnostic tools](#diagnostic-tools).
 
 ![App Service Diagnose and solve problems homepage with diagnostic search box, Risk Alerts assessments, and Troubleshooting categories for discovering diagnostics for the selected Azure Resource.](./media/app-service-diagnostics/app-service-diagnostics-homepage-1.png)
 

articles/azure-monitor/containers/container-insights-update-metrics.md

@@ -83,7 +83,8 @@ To update a specific cluster in your subscription by using Azure CLI, run the fo
 ```azurecli
 az login
 az account set --subscription "<subscriptionName>"
-az aks show -g <resourceGroupName> -n <clusterName> 
+az aks show -g <resourceGroupName> -n <clusterName> --query "servicePrincipalProfile"
+az aks show -g <resourceGroupName> -n <clusterName> --query "addonProfiles.omsagent.identity"
 az role assignment create --assignee <clientIdOfSPN> --scope <clusterResourceId> --role "Monitoring Metrics Publisher" 
 ```
 
@@ -93,7 +94,8 @@ To get the value for `clientIdOfSPNOrMsi`, you can run the command `az aks show`
 ```azurecli
 az login
 az account set --subscription "<subscriptionName>"
-az aks show -g <resourceGroupName> -n <clusterName> 
+az aks show -g <resourceGroupName> -n <clusterName> --query "servicePrincipalProfile"
+az aks show -g <resourceGroupName> -n <clusterName> --query "addonProfiles.omsagent.identity" 
 az role assignment create --assignee <clientIdOfSPNOrMsi> --scope <clusterResourceId> --role "Monitoring Metrics Publisher"
 ```
 

articles/machine-learning/concept-ml-pipelines.md

@@ -30,7 +30,7 @@ The core of a machine learning pipeline is to split a complete machine learning
 
 Machine learning operation (MLOPs) automates the process of building machine learning models and taking the model to production. This is a complex process. It usually requires collaboration from different teams with different skills. A well-defined machine learning pipeline can abstract this complex process into a multiple steps workflow, mapping each step to a specific task such that each team can work independently.  
 
-For example, a typical machine learning project includes the steps of data collection, data preparation, model training, model evaluation, and model deployment. Usually, the data engineers concentrate on data steps, data scientists spend most time on model training and evaluation, the machine learning engineers are focus on model deployment and automation of the entire workflow. By leveraging machine learning pipeline, each team only needs to work on building their own steps. The best way of building steps is using [Azure Machine Learning component](concept-component.md), a self-contained piece of code that does one step in a machine learning pipeline. All these steps built by different users are finally integrated into one workflow through the pipeline definition. The pipeline is a collaboration tool for everyone in the project. The process of defining a pipeline and all its steps can be standardized by each company's preferred DevOps practice. The pipeline can be further versioned and automated. If the ML projects are described as a pipeline, then the best MLOps practice is already applied.  
+For example, a typical machine learning project includes the steps of data collection, data preparation, model training, model evaluation, and model deployment. Usually, the data engineers concentrate on data steps, data scientists spend most time on model training and evaluation, the machine learning engineers focus on model deployment and automation of the entire workflow. By leveraging machine learning pipeline, each team only needs to work on building their own steps. The best way of building steps is using [Azure Machine Learning component](concept-component.md), a self-contained piece of code that does one step in a machine learning pipeline. All these steps built by different users are finally integrated into one workflow through the pipeline definition. The pipeline is a collaboration tool for everyone in the project. The process of defining a pipeline and all its steps can be standardized by each company's preferred DevOps practice. The pipeline can be further versioned and automated. If the ML projects are described as a pipeline, then the best MLOps practice is already applied.  
 
 ### Training efficiency and cost reduction