intro clarity

Author: nibaccam

articles/machine-learning/how-to-enable-app-insights.md

@@ -15,7 +15,11 @@ ms.date: 03/12/2020
 # Monitor and collect data from ML web service endpoints
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
-In this article, you learn how to collect data from and monitor models deployed to web service endpoints in Azure Kubernetes Service (AKS) or Azure Container Instances (ACI) by enabling Azure Application Insights. In addition to collecting an endpoint's output data and response, you can monitor:
+In this article, you learn how to collect data from and monitor models deployed to web service endpoints in Azure Kubernetes Service (AKS) or Azure Container Instances (ACI) by enabling Azure Application Insights via 
+* [Azure Machine Learning Python SDK](#python)
+* [Azure Machine Learning studio](#studio) at https://ml.azure.com
+
+In addition to collecting an endpoint's output data and response, you can monitor:
 
 * Request rates, response times, and failure rates
 * Dependency rates, response times, and failure rates
@@ -29,6 +33,7 @@ In this article, you learn how to collect data from and monitor models deployed
 * If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://aka.ms/AMLFree) today
 
 * An Azure Machine Learning workspace, a local directory that contains your scripts, and the Azure Machine Learning SDK for Python installed. To learn how to get these prerequisites, see [How to configure a development environment](how-to-configure-environment.md)
+
 * A trained machine learning model to be deployed to Azure Kubernetes Service (AKS) or Azure Container Instance (ACI). If you don't have one, see the [Train image classification model](tutorial-train-models-with-aml.md) tutorial
 
 ## Web service metadata and response data
@@ -38,6 +43,8 @@ In this article, you learn how to collect data from and monitor models deployed
 
 The metadata and response to the service - corresponding to the web service metadata and the model's predictions - are logged to the Azure Application Insights traces under the message `"model_data_collection"`. You can query Azure Application Insights directly to access this data, or set up a [continuous export](https://docs.microsoft.com/azure/azure-monitor/app/export-telemetry) to a storage account for longer retention or further processing. Model data can then be used in the Azure Machine Learning to set up labeling, retraining, explainability, data analysis, or other use. 
 
+<a name="python"></a>
+
 ## Use Python SDK to configure 
 
 ### Update a deployed service
@@ -80,7 +87,11 @@ To disable Azure Application Insights, use the following code:
 ## replace <service_name> with the name of the web service
 <service_name>.update(enable_app_insights=False)
 ```
+
+<a name="studio"></a>
+
 ## Use Azure Machine Learning studio to configure
+
 You can also enable Azure Application Insights from Azure Machine Learning studio when you're ready to deploy your model with these steps.
 
 1. Sign in to your workspace at https://ml.azure.com/