Merge pull request #100562 from ScarlettFalls/end2end-setup

End to End tutorial updates - 02 setup

Author: American-Dipper

articles/iot-edge/media/tutorial-machine-learning-edge-02-prepare-environment/custom-endpoints.png

@@ -4,24 +4,23 @@ description: 'Tutorial: Prepare your environment for development and deployment
 author: kgremban
 manager: philmea
 ms.author: kgremban
-ms.date: 11/11/2019
+ms.date: 1/23/2020
 ms.topic: tutorial
 ms.service: iot-edge
 services: iot-edge
 ---
-
 # Tutorial: Set up an environment for machine learning on IoT Edge
 
 > [!NOTE]
 > This article is part of a series for a tutorial about using Azure Machine Learning on IoT Edge. If you have arrived at this article directly, we encourage you to begin with the [first article](tutorial-machine-learning-edge-01-intro.md) in the series for the best results.
 
-This article from the end-to-end Azure Machine Learning on IoT Edge tutorial helps you prepare your environment for development and deployment. First, set up a development machine with all the tools you need. Then, create the necessary cloud resources in Azure.
+This article helps you prepare your environment for development and deployment. First, set up a development machine with all the tools you need. Then, create the necessary cloud resources in Azure.
 
-## Set up a development machine
+## Set up the development VM
 
 This step is typically performed by a cloud developer. Some of the software may also be helpful for a data scientist.
 
-Over the course of this article we perform various developer tasks including coding, compiling, configuring, and deploying IoT Edge modules and IoT devices. For ease of use, we created a PowerShell script that creates an Azure virtual machine with many of the prerequisites already configured. The VM that we create needs to be able to handle [nested virtualization](https://docs.microsoft.com/azure/virtual-machines/windows/nested-virtualization), which is why we chose a [Standard_D8s_v3](../virtual-machines/windows/sizes-general.md#dsv3-series-1) machine size.
+We created a PowerShell script that creates an Azure virtual machine with many of the prerequisites already configured. The VM that we create needs to be able to handle [nested virtualization](https://docs.microsoft.com/azure/virtual-machines/windows/nested-virtualization), which is why we chose a [Standard_D8s_v3](../virtual-machines/windows/sizes-general.md#dsv3-series-1) machine size.
 
 The development VM will be set up with:
 
@@ -45,102 +44,95 @@ The developer VM is not strictly necessary – all the development tools can be
 
 It takes about 30 minutes to create and configure the virtual machine.
 
-### Get the script
-
-Clone or download the PowerShell script from the [Machine Learning and IoT Edge](https://github.com/Azure-Samples/IoTEdgeAndMlSample) sample repository.
-
-### Create an Azure virtual machine
+1. Clone or download the [Machine Learning and IoT Edge](https://github.com/Azure-Samples/IoTEdgeAndMlSample) sample repository to your local computer.
 
-The DevVM directory contains the files needed to create an Azure virtual machine appropriate for completing this tutorial.
-
-1. Open Powershell as an administrator and navigate to the directory where you downloaded the code. We will refer to the root directory for your source as `<srcdir>`.
+1. Open Powershell as an administrator and navigate to the **\IoTEdgeAndMlSample\DevVM** directory located under the root directory where you downloaded the code. We will refer to the root directory for your source as `srcdir`.
 
     ```powershell
-    cd <srcdir>\IoTEdgeAndMlSample\DevVM
+    cd c:\srcdir\IoTEdgeAndMlSample\DevVM
     ```
 
-2. Run the following command to allow execution of scripts. Choose **Yes to All** when prompted.
+   The DevVM directory contains the files needed to create an Azure virtual machine appropriate for completing this tutorial.
+
+1. Run the following command to allow execution of scripts. Choose **Yes to All** when prompted.
 
     ```powershell
     Set-ExecutionPolicy Bypass -Scope Process
     ```
 
-3. Run Create-AzureDevVM.ps1 from this directory.
+1. Run Create-AzureDevVM.ps1.
 
     ```powershell
     .\Create-AzureDevVm.ps1
     ```
 
-    * When prompted, provide the following information:
-      * **Azure Subscription ID**: Your subscription ID, which can be found in the Azure portal
-      * **Resource Group Name**: The name of a new or existing resource group in Azure
-      * **Location**: Choose an Azure location where the virtual machine will be created. For example, westus2 or northeurope. For more information, see [Azure locations](https://azure.microsoft.com/global-infrastructure/locations/).
-      * **AdminUsername**: Provide a memorable name for the admin account you want to create and use on the virtual machine.
-      * **AdminPassword**: Set a password for the admin account on the virtual machine.
+    When prompted, provide the following information:
 
-    * If you do not have Azure PowerShell installed, the script will install [Azure PowerShell Az module](https://docs.microsoft.com/powershell/azure/new-azureps-module-az?view=azps-1.1.0)
+    * **Azure Subscription ID**: Your subscription ID, which can be found in [Azure Subscriptions](https://ms.portal.azure.com/#blade/Microsoft_Azure_Billing/SubscriptionsBlade) in portal.
+    * **Resource Group Name**: The name of a new or existing resource group in Azure.
+    * **Location**: Choose an Azure location where the virtual machine will be created. For example, 'West US 2' or 'North Europe'. For more information, see [Azure locations](https://azure.microsoft.com/global-infrastructure/locations/).
+    * **Username**: Provide a memorable name for the administrator account for the VM.
+    * **Password**: Set a password for the administrator account for the VM.
 
-    * You will be prompted to sign in to Azure.
-
-    * The script confirms the information for the creation of your VM. Press `y` or `Enter` to continue.
+   The script runs for several minutes as it executes the following steps:
 
-The script runs for several minutes as it executes the following steps:
+    1. Installs the [Azure PowerShell Az module](https://docs.microsoft.com/powershell/azure/new-azureps-module-az?view=azps-1.1.0).
+    1. Prompts you to sign in to Azure.
+    1. Confirms the information for the creation of your VM. Press **y** or **Enter** to continue.
+    1. Creates the resource group if it does not exist.
+    1. Deploys the virtual machine.
+    1. Enables Hyper-V on the VM.
+    1. Installs software need for development and clone the sample repository.
+    1. Restarts the VM.
+    1. Creates an RDP file on your desktop for connecting to the VM.
 
-* Create the resource group if it does not exist
-* Deploy the virtual machine
-* Enable Hyper-V on the VM
-* Install software need for development and clone the sample repository
-* Restart the VM
-* Create an RDP file on your desktop for connecting to the VM
+   If you are prompted for the name of the VM to restart it, you can copy its name from the script output. The output also shows the path to the RDP file for connecting to the VM.
 
 ### Set auto-shutdown schedule
 
-To help you reduce cost, the VM has been created with an automatic shutdown schedule that is set to 1900 PST. You may need to update this timing depending on your location and schedule. To update the shutdown
-schedule:
+To help you reduce cost, the development VM has been created with an automatic shutdown schedule that is set to 1900 PST. You may need to update this setting depending on your location and schedule. To update the shutdown schedule:
 
-1. Sign in to the [Azure portal](https://portal.azure.com).
+1. In the Azure portal, navigate to the VM that the script created.
 
-2. Navigate to your virtual machine in the resource group that you provided in the previous section.
+1. From the left pane menu, under **Operations**, select **Auto-shutdown**.
 
-3. Select **Auto-shutdown** on the side navigator.
+1. Adjust the **Scheduled shutdown** and **Time zone** as desired and select **Save**.
 
-4. Enter a new shutdown time in **Scheduled shutdown** or change the **Time zone** then click **Save**.
-
-### Connect and configure development machine
+## Connect to the development VM
 
 Now that we have created a VM we need to finish installing the software needed to complete the tutorial.
 
-#### Start a remote desktop session
-
-1. The VM creation script created an RDP file on your desktop.
+1. Double-click on the RDP file that the script created on your desktop.
 
-2. Double-click on the file with the name **\<Azure VM name\>.rdp**.
+1. You will be presented with a dialog saying the publisher of the remote connection is unknown. This is acceptable, so select **Connect**.
 
-3. You will be presented with a dialog saying the publisher of the remote connection is unknown. Click the **Don’t ask me again for connections to this computer** checkbox then select **Connect**.
+1. Provide the administrator password that you provided to create the VM and click **OK**.
 
-4. When prompted, provide the AdminPassword that you used when running the script to set up the VM and click **OK**.
+1. You will be prompted to accept the certificate for the VM. Select **Yes**.
 
-5. You will be prompted to accept the certificate for the VM. Select **Don’t ask me again for connections to this computer** and choose **Yes**.
-
-#### Install Visual Studio Code extensions
+## Install Visual Studio Code extensions
 
 Now that you have connected to the development machine, add some useful extensions to Visual Studio Code to make the development experience easier.
 
-1. In a PowerShell window, navigate to **C:\\source\\IoTEdgeAndMlSample\\DevVM**.
+1. Connect to the development VM, open a PowerShell window, and navigate to the **C:\source\IoTEdgeAndMlSample\DevVM** directory. This directory was created by the script that created the VM.
 
-2. Allow scripts to be executed on the virtual machine by typing.
+    ```powershell
+    cd C:\source\IoTEdgeAndMlSample\DevVM
+    ```
+
+1. Run the following command to allow execution of scripts. Choose **Yes to All** when prompted.
 
     ```powershell
-    Set-ExecutionPolicy Bypass -Scope CurrentUser -Force
+    Set-ExecutionPolicy Bypass -Scope Process
     ```
 
-3. Run the script.
+1. Run the Visual Studio Code extensions script.
 
     ```powershell
     .\Enable-CodeExtensions.ps1
     ```
 
-4. The script will run for a few minutes installing VS code extensions:
+1. The script will run for a few minutes installing VS code extensions:
 
     * Azure IoT Tools
     * Python
@@ -152,19 +144,15 @@ Now that you have connected to the development machine, add some useful extensio
 
 These steps are typically performed by a cloud developer.
 
-Azure IoT Hub is the heart of any IoT application. It handles secure communication between IoT devices and the cloud. It is the main coordination point for the operation of the IoT Edge machine learning solution.
+Azure IoT Hub is the heart of any IoT application as it handles secure communication between IoT devices and the cloud. It is the main coordination point for the operation of the IoT Edge machine learning solution.
 
-* IoT Hub uses routes to direct incoming data from IoT devices to other downstream services. We will take advantage of IoT Hub routes to send device data to Azure Storage where it can be consumed by Azure Machine Learning to train our remaining useful life (RUL) classifier.
+* IoT Hub uses routes to direct incoming data from IoT devices to other downstream services. We will take advantage of IoT Hub routes to send device data to Azure Storage. In Azure Storage, the device data is consumed by Azure Machine Learning to train our remaining useful life (RUL) classifier.
 
 * Later in the tutorial, we will use IoT Hub to configure and manage our Azure IoT Edge device.
 
-In this section, you use a script to create an Azure IoT hub and an Azure Storage account. Then, you configure a route that forwards data received by the hub to an Azure Storage Blob container using the Azure portal. These steps take about 10 minutes to complete.
+In this section, you use a script to create an Azure IoT hub and an Azure Storage account. Then in the Azure portal, you configure a route that forwards data received by the hub to an Azure Storage container. These steps take about 10 minutes to complete.
 
-### Create cloud resources
-
-1. On your development machine, open a PowerShell window.
-
-1. Change to the IoTHub directory.
+1. Connect to the development VM, open a PowerShell window, and navigate to the **IoTHub** directory.
 
     ```powershell
     cd C:\source\IoTEdgeAndMlSample\IoTHub
@@ -173,50 +161,50 @@ In this section, you use a script to create an Azure IoT hub and an Azure Storag
 1. Run the creation script. Use the same values for subscription ID, location, and resource group as you did when creating the development VM.
 
     ```powershell
-    .\New-HubAndStorage.ps1 -SubscriptionId <subscription id> -Location
-    <location> -ResourceGroupName <resource group>
+    .\New-HubAndStorage.ps1 -SubscriptionId <subscription id> -Location <location> -ResourceGroupName <resource group>
     ```
 
     * You will be prompted to sign in to Azure.
-    * The script confirms the information for the creation of your Hub and Storage account. Press `y` or `Enter` to continue.
+    * The script confirms the information for the creation of your Hub and Storage account. Press **y** or **Enter** to continue.
 
-The script takes about two minutes to run. Once complete, the script outputs the name of the hub and the storage account.
+The script takes about two minutes to run. Once complete, the script outputs the name of the IoT hub and the storage account.
 
-### Review route to storage in IoT Hub
+## Review route to storage in IoT Hub
 
-As part of creating the IoT hub, the script that we ran in the previous section also created a custom endpoint and a route. IoT Hub routes consist of a query expression and an endpoint. If a message matches the expression, the data is sent along the route to the associated endpoint. Endpoints can be Event Hubs, Service Bus Queues, and Topics. In this case, the endpoint is a Blob container in a storage account. Let’s use the Azure portal to review the route created by our script.
+As part of creating the IoT hub, the script that we ran in the previous section also created a custom endpoint and a route. IoT Hub routes consist of a query expression and an endpoint. If a message matches the expression, the data is sent along the route to the associated endpoint. Endpoints can be Event Hubs, Service Bus Queues, and Topics. In this case, the endpoint is a blob container in a storage account. Let’s use the Azure portal to review the route created by our script.
 
-1. Open the [Azure portal](https://portal.azure.com).
+1. Open the [Azure portal](https://portal.azure.com) and go to the resource group you're using for this tutorial.
 
-1. Choose all services from the left navigator, type IoT in the search box, and select **IoT Hub**.
+1. In the list of resources, select the IoT Hub that the script created. It will have a name ending with random characters such as `IotEdgeAndMlHub-jrujej6de6i7w`.
 
-1. Choose the IoT Hub created in the previous step.
+1. From the left pane menu, under **Messaging**, select **Message routing**.
 
-1. In the IoT Hub side navigator, choose **Message routing**.
+1. On the **Message routing** page, select the **Custom endpoints** tab.
 
-1. The message routing page has two tabs, **Routes** and **Custom endpoints**. Select the **Custom endpoints** tab.
+1. Expand the **Storage** section:
 
-1. Under **Blob storage**, select **turbofanDeviceStorage**.
+   ![Verify turbofandevicestorage is in the custom endpoints list](media/tutorial-machine-learning-edge-02-prepare-environment/custom-endpoints.png)
 
-1. Note that this endpoint points to a blob container called **devicedata** in the storage account created in the last step, which is named **iotedgeandml\<unique suffix\>**.
+   We see **turbofandevicestorage** is in the custom endpoints list. Note the following characteristics about this endpoint:
 
-1. Also note the **Blob file name format** has been changed from the default format to instead place the partition as the last element in the name. We find this format is more convenient for the file operations we will do with Azure Notebooks later in the tutorial.
-
-1. Close the endpoint details blade to return to the **Message routing** page.
+   * It points to the blob storage container you created named `devicedata` as indicated by **Container name**.
+   * Its **Filename format** has partition as the last element in the name. We find this format is more convenient for the file operations we will do with Azure Notebooks later in the tutorial.
 
 1. Select the **Routes** tab.
 
 1. Select the route named **turbofanDeviceDataToStorage**.
 
-1. Note that the route’s endpoint is the **turbofanDeviceStorage** custom endpoint.
+1. On the **Routes details** page, note that the route’s endpoint is the **turbofanDeviceStorage** endpoint.
+
+   ![Review details about the turbofanDeviceDataToStorage route](media/tutorial-machine-learning-edge-02-prepare-environment/route-details.png)
 
-1. Look at the routing query, which is set to **true**. This means that all device telemetry messages will match this route and therefore all messages will be sent to the **turbofanDeviceStorage** endpoint.
+1. Look at the **Routing query**, which is set to **true**. This setting means that all device telemetry messages will match this route; and therefore all messages will be sent to the **turbofanDeviceStorage** endpoint.
 
-1. Close the route details.
+1. Since no edits were made, just close this page.
 
 ## Next steps
 
-In this article, we created an IoT Hub and configured a route to an Azure Storage account. In the next article, we will send data from a set of simulated devices through the IoT Hub into the storage account. Later in the tutorial, after we have configured our IoT Edge device and modules, we will revisit routes and look a little more at the routing query.
+In this article, we created an IoT Hub and configured a route to an Azure Storage account. Next, we will send data from a set of simulated devices through the IoT Hub into the storage account. Later in the tutorial, after we have configured our IoT Edge device and modules, we will revisit routes and look a little more at the routing query.
 
 For more information about the steps covered in this portion of the Machine Learning on IoT Edge tutorial, see: