Merge pull request #11954 from msgregman/patch-1

minor fixes across azure ml tutorial

Author: ktoliver

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. 
+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.
 
@@ -36,16 +36,16 @@ 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. 
-* Module 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).
+* 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
 
 >[!NOTE]
 >
 > While in preview, Azure Machine Learning does not support the process identification security feature enabled by default with IoT Edge. 
-> Below are the steps to disable it. This is however not suitable for use in production.
+> Below are the steps to disable it. This is however not suitable for use in production. These steps are only necessary on Linux, as you will have completed this during the Windows Edge runtime setup steps.
 
-To disable process identification, you'll need to provide the ip address and port for **workload_uri** and **management_uri** in the **connect** section of the IoT Edge daemon configuration.
+To disable process identification on your IoT edge device, you'll need to provide the ip address and port for **workload_uri** and **management_uri** in the **connect** section of the IoT Edge daemon configuration.
 
 Get the IP address first. Enter `ifconfig` in your command line and copy the IP address of the **docker0** interface.
 
@@ -126,7 +126,7 @@ Check that your container image was successfully created and stored in the Azure
 
 1. Add the machine learning module that you created.
 
-    1. Click **Add** and select **Azure Machine Learning Module**.
+    1. Click **Add** and select **IoT Edge Module**.
     1. In the **Name** field, enter `machinelearningmodule`
     1. In the **Image** field, enter your image address; for example `<registry_name>.azurecr.io/machinelearningmodule:1`.
     1. Select **Save**.