Machine Learning with Azure ML Studio

Directions on How to Build the Predictive Model In Microsoft Azure ML

• Sign in to Microsoft Azure using your login credentials in the  Azure portal 
• Create a workspace for you to store your work
  • In the upper-left corner of Azure portal, select + Create a resource.
  • Use the search bar to type Machine Learning.
  • Select Machine Learning.
  • In the Machine Learning pane, select Create to begin.
  • You will provide the following information below to configure your new workspace:
    • Subscription – Select the Azure subscription that you would like to use.
    • Resource group – Create a name for your resource group which will hold resources for your Azure solution.
    • Workspace name – Create a unique name that identifies your workspace.
    • Region – select the region closest to the users to reduce latency
    • Storage account – created by default
    • Key Vault – created by default
    • Application insights – created by default
  • When you have completed configuring the workspace, select Review + Create.
  • Review the settings and make any additional changes or corrections. Lastly, select Create. When deployment of workspaces has completed you will see the message “Your deployment is Complete”. Please see the visual below as a reference. 

• To Launch your workspace, click Go to resource
• Next, Click the blue Launch Studio button which is under Manage your Machine Learning Lifecycle. Now you are ready to begin!!!!

• Click on Experiments in the left panel
• Click on NEW in the lower left corner 
• Select Blank Experiment. The new experiment is created with a default name. You can change the name at the top of the page. 
• Upload the data above into Ml studio
  • Drag the datasets on to the experiment canvas. (We uploaded preprocessed data
  • If you would like to see what the data looks like, click on the outpost port at the bottom on the dataset and select Visualize. Given this data we are going to try and predict if there the IoT sensors have communication errors. 
• Next, prepare the data
  • Remove unnecessary columns /data
    • Type “Select Columns” in the Search box  and select Select Columns in the Dataset  module, then drag and drop it on the canvas. This allows you to exclude any columns that you do not want in the model. 
    • Connect Select Columns in Dataset to the Data on the canvas.
  • Choose and Apply a Learning Algorithm
    • Click on Data Transformation in the left column 
      • Next, click on the drop down Manipulation 
      • Drag the Select Edit the Metadata (use this to change the metadata that is associated with columns inside the dataset. This changes the metadata inside Azure Machine Learning that tells the downstream components how to use the selected columns.)
    • Split the data 
      • Then, click on the drop down Sample and Split.
      • Choose Split Data and add it to the canvas and connect it to Edit the Metadata.
      • Click on Split Data and find the Fraction of rows in the output dataset and set it to .80. You are splitting the data to train the model using 80% of the data and test the model using 20% of the data.
• Then you train the data 
  • Choose the drop down under Machine Learning
  • Choose the drop down under Initialize Model
  • Choose the drop down under Anomaly Detection 
  • Click on PCA- Based Anomaly Detection and add this to the canvas and connect with the Split data.  
  • Choose the drop down under Machine Learning
  • Choose the drop down under Initialize Model
  • Choose the drop down under Anomaly Detection 
  • Click on One-Class Support Vector machine and add this to the canvas and connect with the Split data.  
  • Choose the drop down under Machine Learning
  • Then, choose the drop down under Train
  • Click on Tune Model Hyperparameters and add this to the canvas and connect with the Split Data.
  • Choose the drop down under Machine Learning
  • Then, choose the drop down under Train
  • Click on Train Anomaly Detection Model
• Then score the model 
  • Choose the drop down under Machine Learning
  • Then, choose the drop down – Score
  • Click on Score Model
• Normalize the data
  • Choose the drop down under Data Transformation
  • Then, choose the drop down under Scale and Reduce
  • Click on Normalize Data
• Evaluate the model – this will compare the one-class SVM and PCA – based anomaly detectors.
  • Choose the drop down under Machine Learning
  • Then, choose the drop down under Evaluate
  • Click on Evaluate Model
• Click Run at the bottom of the screen to run the experiment. Below is how the model should look. Please click on the link to use our experiment (Experiment Name: IOT Anomaly Detection) for further reference.  This link requires that you have a Azure ML account.  To access the gallery, click the following public link:  https://gallery.cortanaintelligence.com/Experiment/IOT-Anomaly-Detection

Derek Moore, Erica Davis, and Hank Galbraith, authors.