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Author: JKirsch1

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

@@ -63,7 +63,7 @@ The authentication process tries the following methods, one after another, until
 
 [!INCLUDE [mlflow-configure-auth](includes/machine-learning-mlflow-configure-auth.md)]
 
-If you'd rather use a certificate instead of a secret, you can configure the following environment variables:
+If you'd rather use a certificate than a secret, you can configure the following environment variables:
 
 - Set `AZURE_CLIENT_CERTIFICATE_PATH` to the path of a file that contains the certificate and private key pair in Privacy Enhanced Mail (PEM) or Public-Key Cryptography Standards 12 (PKCS #12) format.
 - Set `AZURE_CLIENT_CERTIFICATE_PASSWORD` to the password of the certificate file, if it uses a password.
@@ -97,15 +97,15 @@ All MLflow runs are logged to the active experiment. By default, runs are logged
 
 > [!TIP]
 >
-> When you use the Azure Machine Learning CLI v2, to submit jobs, you can set the experiment name by using the `experiment_name` property in the YAML definition of the job. You don't have to configure it in your training script. For more information, see [YAML: display name, experiment name, description, and tags](reference-yaml-job-command.md#yaml-display-name-experiment-name-description-and-tags).
+> When you use the Azure Machine Learning CLI v2 to submit jobs, you can set the experiment name by using the `experiment_name` property in the YAML definition of the job. You don't have to configure it in your training script. For more information, see [YAML: display name, experiment name, description, and tags](reference-yaml-job-command.md#yaml-display-name-experiment-name-description-and-tags).
 
 
 # [MLflow SDK](#tab/mlflow)
 
 Use the MLflow [`mlflow.set_experiment()`](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.set_experiment) command to configure your experiment.
 
 ```python
-experiment_name = 'experiment_with_mlflow'
+experiment_name = "experiment_with_mlflow"
 mlflow.set_experiment(experiment_name)
 ```
 

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

@@ -9,7 +9,7 @@ ms.author: fasantia
 For interactive jobs where there's a user connected to the session, you can rely on interactive authentication. No further action is required.
 
 > [!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.
+> *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.
 
@@ -36,4 +36,4 @@ 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 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.

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

@@ -58,9 +58,9 @@ ms.author: fasantia
           from azure.identity import DefaultAzureCredential
     
           # Enter information about your Azure Machine Learning workspace.
-          subscription_id = '<subscription-ID>'
-          resource_group = '<resource-group-name>'
-          workspace_name = '<workspace-name>'
+          subscription_id = "<subscription-ID>"
+          resource_group = "<resource-group-name>"
+          workspace_name = "<workspace-name>"
     
           ml_client = MLClient(credential=DefaultAzureCredential(),
                                   subscription_id=subscription_id, 
@@ -97,10 +97,10 @@ ms.author: fasantia
     > If you use a private link-enabled workspace, the MLflow endpoint also uses a private link to communicate with Azure Machine Learning. As a result, the tracking URI uses a format that's different from the one in this article. In this case, you need to use the Azure Machine Learning SDK for Python or the Azure Machine Learning CLI v2 to get the tracking URI.
 
     ```python
-    region = '<region>'
-    subscription_id = '<subscription-ID>'
-    resource_group = '<resource-group-name>'
-    workspace_name = '<workspace-name>'
+    region = "<region>"
+    subscription_id = "<subscription-ID>"
+    resource_group = "<resource-group-name>"
+    workspace_name = "<workspace-name>"
     
     mlflow_tracking_uri = f"azureml://{region}.api.azureml.ms/mlflow/v1.0/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.MachineLearningServices/workspaces/{workspace_name}"
     ```