Tuning Hyperparameters in Azure Machine Learning

1m 22s

In Tuning Hyperparameters in Azure Machine Learning, we see how to use hyperparameters to arrive at an optimal model solution. The training process is somewhat trial and error, so we start by looking at hyperparameter value selection. Then, we see how to run multiple trials with those values and, finally, how to get the best result without exhaustively trialling all possible hyperparameter values.

Learning Objectives

  • Hyperparameters overview
  • Sweep jobs
  • Early termination strategies

Intended Audience

Students preparing for the DP-100: Designing and Implementing a Data Science Solution on Azure exam and those who want to learn how to optimize model training with input or hyperparameter variables.


Familiarity with data science concepts such as:

  • Models
  • Statistical analysis
  • Command jobs

It will be helpful to have taken the Running and Monitoring Training Scripts in Azure Machine Learning lesson.

About the Author
Learning paths

Hallam is a software architect with over 20 years experience across a wide range of industries. He began his software career as a  Delphi/Interbase disciple but changed his allegiance to Microsoft with its deep and broad ecosystem. While Hallam has designed and crafted custom software utilizing web, mobile and desktop technologies, good quality reliable data is the key to a successful solution. The challenge of quickly turning data into useful information for digestion by humans and machines has led Hallam to specialize in database design and process automation. Showing customers how leverage new technology to change and improve their business processes is one of the key drivers keeping Hallam coming back to the keyboard.