Exploring AML Designer Transforms: Add Columns

30 minutes
  • 3 Learning Objectives

About this Hands-on Lab

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 `Add Columns` module to gain a deeper understanding of the tools at your disposal.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

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.

Explore Add Columns

Add CRM Dataset Shared and CRM Upselling Labels Shared dataset nodes to the canvas. Visualize each node to see the raw dataset. Take note of the row and column counts.

These two datasets are the same size, and already ordered. Use an Add Columns node to combine these into a single dataset.

Submit the Pipeline to perform the transformation.

Visualize the Transformed Data

When the pipeline finishes, inspect the output of the Add Columns node. How many rows and columns are now present in the dataset?

Additional Resources

Storing labels separately from feature data is a common practice for multilabel problems. This allows for multiple models to be trained on the features without the extra work of removing extraneous labels from the dataset. Machine Learning Studio provides the Add Columns module to combine features and labels, or multiple feature datasets, together into a single dataset ready for training your models.

Lab Goals

  1. Set up the Workspace
  2. Explore Add Columns
  3. Visualize the Transformed Data

Logging in to the lab environment

To avoid issues with the lab, use a new Incognito or Private browser window to log in to the lab. This ensures that your personal account credentials, which may be active in your main window, are not used for the lab.

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