- Lab
-
Libraries: If you want this lab, consider one of these libraries.
- 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.
Lab Info
Table of Contents
-
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.
-
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.
-
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.
Real skill practice before real-world application
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.
Learn by doing
Engage hands-on with the tools and technologies you’re learning. You pick the skill, we provide the credentials and environment.
Follow your guide
All labs have detailed instructions and objectives, guiding you through the learning process and ensuring you understand every step.
Turn time into mastery
On average, you retain 75% more of your learning if you take time to practice. Hands-on labs set you up for success to make those skills stick.