- Lab
- AI
- Cloud
Tune Hyperparameters Using the Azure ML Python SDK
In this hands-on lab, you will become familiar with performing the basic actions for hyperparameter tuning.
Path Info
Table of Contents
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Challenge
Create a Compute Resource and Access the Provided Jupyter Notebook
Utilize the provisioned workspace to create a compute instance. This will be used to power a Jupyter Notebook and execute your machine learning tasks. When setting up the compute instance, choose the smallest "general purpose" VM size among the recommended options.
If you plan to utilize the existing Jupyter Notebook with the data and steps required to create and run the experiment already preconfigured, clone the repo from GitHub.
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Challenge
Simulate Sample Data
Generate random data to simulate the diabetes patient information.
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Challenge
Create a Training Script Using Hyperparameters
Prepare a training script that will train the model using hyperparameters.
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Challenge
Find and Register the Best Model
Run a hyperdrive experiment to find the best performing model, and then register it.
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.