- Lab
- AI
- Cloud
Create a Machine Learning Pipeline Using Azure SDK
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.
Path Info
Table of Contents
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Challenge
Create a Workspace
Create a workspace in which you will build your diabetes research pipeline. Use the same location as your lab-provided resource group for all resources.
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Challenge
Create Compute Resources
Create a compute instance to both run your Notebook and execute the pipeline workload. When prompted to choose a VM for compute, select the smallest "general purpose" machine.
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Challenge
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.
What's a lab?
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.
Provided environment for hands-on practice
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!
Did you know?
On average, you retain 75% more of your learning if you get time for practice.