Use Automated ML from the Azure Machine Learning Studio

1 hour
  • 3 Learning Objectives

About this Hands-on Lab

In this hands-on lab, you will become familiar with Automated ML and using it to build machine learning models.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

Create a Dataset

Utilize one of the publicly available open datasets provided by Azure, such as the San Francisco Safety Data dataset.

Create an Experiment

Create and configure an experiment to train a classification model. To keep the lab within a reasonable timeframe, set the timeout for 15 minutes.

Complete an Automated ML Run

Let the Automated ML run do its magic, and observe as it saves you and your team many tedious hours.

Additional Resources

Lab Scenario

In this hands-on lab scenario, you are a Data Scientist for Awesome Company. Recently, the company has been using the Azure Machine Learning service to implement and run their machine learning workloads. You plan to take advantage of Azure's Automated ML feature to drastically reduce both the time involved in model development and the team's required skill level to carry out these tasks.

To accomplish your goal, the following should be completed:

  • Create a dataset
  • Create an experiment
  • Complete an Automated ML run

What are Hands-on Labs

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

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Psst…this one if you’ve been moved to ACG!

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