- Lab
- A Cloud Guru
Exploring AML Designer Transforms: Apply SQL Transformation
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 SQL Transformation* module to gain a deeper understanding of the tools at your disposal.
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Table of Contents
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Challenge
Set up the Workspace
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Log in and go to the Azure Machine Learning Studio workspace provided in the lab.
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Create a training cluster of
D2
instances. -
Create a new blank pipeline in the Azure Machine Learning Studio Designer.
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Challenge
Explore Apply SQL Transformation
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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.
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Using an Apply SQL Transformation transform node, combine the datasets for all movies where the title starts with "The". This will require an Inner Join on the shared data. Only include one of the columns containing the shared data in the output.
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Submit the pipeline to perform the transformation.
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Challenge
Visualize the Transformed Data
When the pipeline finishes, inspect the output of the Apply SQL Transformation node. Did you only get movies starting with "The"? Did you remove the duplicate ID column?
What's a lab?
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We will provide the credentials and environment necessary for you to practice right within your browser.
Guided walkthrough
Follow along with the author’s guided walkthrough and build something new in your provided environment!
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