diff --git a/docs/getting_started.md b/docs/getting_started.md
index 2c63fa94..11bfb20f 100644
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@@ -6,6 +6,7 @@ This guide shows how to get MLOpsPython working with a sample ML project ***diab
 We recommend working through this guide completely to ensure everything is working in your environment. After the sample is working, follow the [bootstrap instructions](../bootstrap/README.md) to convert the ***diabetes_regression*** sample into a starting point for your project.
 
 - [Setting up Azure DevOps](#setting-up-azure-devops)
+  - [Install the Azure Machine Learning extension](#install-the-azure-machine-learning-extension)
 - [Get the code](#get-the-code)
 - [Create a Variable Group for your Pipeline](#create-a-variable-group-for-your-pipeline)
   - [Variable Descriptions](#variable-descriptions)
@@ -33,6 +34,12 @@ You'll use Azure DevOps for running the multi-stage pipeline with build, model t
 
 If you already have an Azure DevOps organization, create a new project using the guide at [Create a project in Azure DevOps and TFS](https://docs.microsoft.com/en-us/azure/devops/organizations/projects/create-project?view=azure-devops).
 
+### Install the Azure Machine Learning extension
+
+Install the **Azure Machine Learning** extension to your Azure DevOps organization from the [Visual Studio Marketplace](https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml).
+
+This extension contains the Azure ML pipeline tasks and adds the ability to create Azure ML Workspace service connections.
+
 ## Get the code
 
 We recommend using the [repository template](https://github.com/microsoft/MLOpsPython/generate), which effectively forks the repository to your own GitHub location and squashes the history. You can use the resulting repository for this guide and for your own experimentation.
@@ -118,8 +125,6 @@ Check that the newly created resources appear in the [Azure Portal](https://port
 
 At this point, you should have an Azure ML Workspace created. Similar to the Azure Resource Manager service connection, you need to create an additional one for the Azure ML Workspace.
 
-Install the **Azure Machine Learning** extension to your Azure DevOps organization from the [Visual Studio Marketplace](https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml). The extension is required for the service connection.
-
 Create a new service connection to your Azure ML Workspace using the [Machine Learning Extension](https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml) instructions to enable executing the Azure ML training pipeline. The connection name needs to match `WORKSPACE_SVC_CONNECTION` that you set in the variable group above.
 
 ![Created resources](./images/ml-ws-svc-connection.png)