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Labs

Using Azure Automated ML

In this hands-on lab, you are working as a data scientist for Sam's Taxi, a taxi cab company. You’ve been asked to come up with a model that can be used to predict fare prices based on length of drive and time it takes to drive there. You’ll use Azure Machine Learning studio and Azure automated machine learning to create a regression model that will predict fare prices. You’ll also get a chance to deploy the model to an Azure Container instance.

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Labs

Path Info

Level
Clock icon Beginner
Duration
Clock icon 1h 30m
Published
Clock icon Dec 16, 2021

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Table of Contents

  1. Challenge

    Open the Azure ML Studio and Create a New Dataset

    In the Azure ML studio, create a new dataset using the taxi fare data file from Microsoft.

  2. Challenge

    Configure and Start the Automated ML Run

    Change the timeout of the run to 30 minutes by clicking on View additional configuration settings, expanding Exit criterion, and setting the Training job time (hours) to 0.5, so it will stop after approximately 30 minutes.

  3. Challenge

    Review Results

    Take a look at some of the metrics for the run, like the R2 score, or the root mean squared error.

  4. Challenge

    Deploy the Model to an Azure Container Instance

    Make sure you click Advanced before deployment, and change both the CPU reserve capacity and Memory reserve capacity to 1, or your resources will be deleted.

  5. Challenge

    Test Your Newly Deployed Model

    Use some values from the CSV file and see how accurate the predicted value is compared to the actual value.

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