updates based on Kelly's feedback

Author: gregman-msft

articles/iot-edge/tutorial-deploy-machine-learning.md

@@ -13,7 +13,7 @@ ms.custom: mvc
 
 # Deploy Azure Machine Learning as an IoT Edge module - preview
 
-You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. This tutorial walks you through deploying an Azure Machine Learning module that predicts when a device fails based on simulated machine temperature data. For more information about Azure ML on IoT Edge, see [Azure Machine Learning documentation](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/use-azure-iot-edge-ai-toolkit).
+You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. This tutorial walks you through deploying an Azure Machine Learning module that predicts when a device fails based on simulated machine temperature data. For more information about Azure ML on IoT Edge, see [Azure Machine Learning documentation](../machine-learning/desktop-workbench/use-azure-iot-edge-ai-toolkit.md).
 
 The Azure Machine Learning module that you create in this tutorial reads the environmental data generated by your device and labels the messages as anomalous or not.
 
@@ -36,7 +36,7 @@ The Azure Machine Learning module does not support ARM processors.
 
 Have the following prerequisites on your development machine: 
 * An Azure Machine Learning account. Follow the instructions in [Create Azure Machine Learning accounts and install Azure Machine Learning Workbench](../machine-learning/service/quickstart-installation.md#create-azure-machine-learning-services-accounts). You do not need to install the workbench application for this tutorial. 
-* Model Management for Azure ML on your machine. To set up your environment and create an account, follow the instructions in [Model management setup](../machine-learning/desktop-workbench/deployment-setup-configuration.md). You do not need to complete the cluster deployment step.
+* Model Management for Azure ML on your machine. To set up your environment and create an account, follow the instructions in [Model management setup](../machine-learning/desktop-workbench/deployment-setup-configuration.md). During deployment setup, it is recommended to choose the local steps instead of cluster, where possible.
 
 ### Disable process identification