Adjust language; add link about service principals

Author: JKirsch1

articles/machine-learning/how-to-use-mlflow-configure-tracking.md

@@ -19,7 +19,7 @@ ms.devlang: azurecli
 
 This article explains how to configure MLflow to connect to an Azure Machine Learning workspace for tracking, registry management, and deployment.
 
-Azure Machine Learning workspaces are MLflow-compatible, which means they can act as MLflow servers without any extra configuration. Each workspace has an MLflow tracking URI that MLflow can use to connect to the workspace. Azure Machine Learning workspaces **are already configured to work with MLflow** so no extra configuration is required.
+Azure Machine Learning workspaces are MLflow-compatible, which means they can act as MLflow servers without any extra configuration. Each workspace has an MLflow tracking URI that MLflow can use to connect to the workspace. Azure Machine Learning workspaces **are already configured to work with MLflow**, so no extra configuration is required.
 
 However, if you work outside Azure Machine Learning, you need to configure MLflow to point to the workspace. Affected environments include your local machine, Azure Synapse Analytics, and Azure Databricks.
 
@@ -56,7 +56,7 @@ By default, the Azure Machine Learning plugin for MLflow performs interactive au
 The authentication process tries the following methods, one after another, until one succeeds:
 
 1. **Environment**: Account information that's specified via environment variables is read and used for authentication.
-1. **Managed Identity**: If the application is deployed to an Azure host with a managed identity enabled, the managed identity is used for authentication.
+1. **Managed identity**: If the application is deployed to an Azure host with a managed identity enabled, the managed identity is used for authentication.
 1. **Azure CLI**: If you use the Azure CLI `az login` command to sign in, your credentials are used for authentication.
 1. **Azure PowerShell**: If you use the Azure PowerShell `Connect-AzAccount` command to sign in, your credentials are used for authentication.
 1. **Interactive browser**: The user is interactively authenticated via the default browser.

articles/machine-learning/includes/machine-learning-mlflow-configure-auth.md

@@ -11,7 +11,9 @@ For interactive jobs where there's a user connected to the session, you can rely
 > [!WARNING]
 > *Interactive browser* authentication blocks code execution when it prompts for credentials. This approach isn't suitable for authentication in unattended environments like training jobs. We recommend that you configure a different authentication mode in those environments.
 
-For scenarios that require unattended execution, you have to configure a service principal to communicate with Azure Machine Learning.
+For scenarios that require unattended execution, you need to configure a service principal to communicate with Azure Machine Learning. For information about creating a service principal, see [Configure a service principal](../how-to-setup-authentication.md#configure-a-service-principal).
+
+Use the tenant ID, client ID, and client secret of your service principal in the following code:
 
 # [MLflow SDK](#tab/mlflow)
 
@@ -36,4 +38,4 @@ export AZURE_CLIENT_SECRET="<Azure-client-secret>"
 > [!TIP]
 > When you work in shared environments, we recommend that you configure these environment variables at the compute level. As a best practice, manage them as secrets in an instance of Azure Key Vault.
 >
-> For instance, in Azure Databricks you can use secrets in environment variables as follows in the cluster configuration: `AZURE_CLIENT_SECRET={{secrets/<scope-name>/<secret-name>}}`. For more information about implementing this approach in Azure Databricks, see [Reference a secret in an environment variable](/azure/databricks/security/secrets/secrets#reference-a-secret-in-an-environment-variable), or refer to documentation for your platform.
+> For instance, in an Azure Databricks cluster configuration, you can use secrets in environment variables in the following way: `AZURE_CLIENT_SECRET={{secrets/<scope-name>/<secret-name>}}`. For more information about implementing this approach in Azure Databricks, see [Reference a secret in an environment variable](/azure/databricks/security/secrets/secrets#reference-a-secret-in-an-environment-variable), or refer to documentation for your platform.