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Exploring AML Designer Transforms: Apply Math Operation

A large amount of time for machine learning tasks is spent understanding the data and getting it into the proper configuration to train the model. This is the Data Wrangling, Exploration, and Cleaning phase of the machine learning lifecycle. In Azure Machine Learning designer, many common data changing operations are provided as transform modules. In this lab, you will explore the Apply Math Operation module to gain a deeper understanding of the tools at your disposal.

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Path Info

Clock icon Advanced
Clock icon 30m
Clock icon Sep 24, 2020

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Table of Contents

  1. Challenge

    Set Up the Workspace

    1. Log in and navigate to the Azure Machine Learning studio workspace provided in the lab.
    2. In Compute, create a Training Cluster:
      • Provide a unique name for the cluster.
      • Use virtual machine size Standard_D2_v2.
      • For minimum and maximum nodes, input 2.
    3. In Designer, start a new pipeline from the easy-to-use prebuilt modules:
      • Select the Training Cluster you created as the default compute for this pipeline.
      • Provide a unique name for your pipeline.
  2. Challenge

    Explore Apply Math Operation

    1. From the Datasets submenu on the left, drag a CRM Upselling Labels Shared node onto the canvas. Visualize the dataset.
    2. From the Data Transformations submenu on the left, drag an Apply Math Operation node onto the canvas.
    3. Connect the output of CRM Upselling Labels Shared to the input of Apply Math Operation.
    4. Click the Apply Math Operation node to configure it:
      • Category: Compare
      • Comparison function: PairMax
      • Value to compare type: Constant
      • Second argument: 0 With PairMax, this will take the maximum value of our provided constant 0 or the column value. For current 1 values, it will choose 1. For the current -1 values, it will choose 0.
      • On Column set, click Edit column, then select Col1 and Save.
      • Change Output mode to Inplace. This will replace the value in the column without adding another column that we'd have to manage after the operation.
    5. Select Submit to submit the pipeline, creating a new experiment.
    6. Once the pipeline completes, right-click the Apply Math Operation node and choose Visualize Result_dataset.
      • There are still 50,000 rows and 1 column, but the values that were -1 are now 0.

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