- Lab
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Libraries: If you want this lab, consider one of these libraries.
- 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.
Lab 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.
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.