Create a Machine Learning Pipeline Using Azure SDK

1 hour
  • 3 Learning Objectives

About this Hands-on Lab

In this hands-on lab, you will become familiar with creating a machine learning workspace, creating compute resources, and programmatically developing a multi-step pipeline in Azure Machine Learning.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

Create a Workspace

Create a workspace in which you will build your diabetes research pipeline.

Create Compute Resources

Create a compute instance to both run your notebook and execute the pipeline workload.

Create and Run a Pipeline

Create a pipeline to prepare and train the diabetes data. It should include multiple steps that call Python scripts and utilize Microsoft’s open diabetes dataset.

To utilize the existing Jupyter notebook with all of the necessary steps to create and run the pipeline already preconfigured, clone the repo from GitHub.

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. As part of this effort, you are learning to create pipelines programmatically using the Azure SDK. You'll be utilizing an open dataset on diabetes in order to help Awesome Company build a pipeline for diabetes research.

To accomplish your goal, the following should be completed:

  • Create a workspace
  • Create compute resources
  • Create and run a pipeline

Lab Setup

To utilize the preconfigured Jupyter notebook, clone the repo from GitHub.

Additional Resources

Overview of SQL Server on Azure Virtual Machines (Linux)

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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