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Exploring AML Designer Transforms: Join Data

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 Join Data 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

    Log in and go to the Azure Machine Learning Studio workspace provided in the lab.

    Create a Training Cluster of D2 instances.

    Create a new blank Pipeline in the Azure Machine Learning Studio Designer.

  2. Challenge

    Explore Join Data

    Add IMDB Movie Titles and Movie Ratings dataset nodes to the canvas. Visualize these datasets to see if they have any common data. Note, these columns might not be named exactly the same in both datasets.

    Using a Join Data transform node, combine the datasets on their shared data. This will be an Inner Join operation. Remove one of the columns containing the shared data.

    Submit the Pipeline to perform the transformation.

  3. Challenge

    Visualize the Transformed Data

    When the pipeline finishes, inspect the output of the Join Data node. Can you tell what movie is being reviewed now? Was the duplicate ID column removed successfully?

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