Configuring and Launching Hyperparameter Tuning Jobs With Amazon SageMaker AMT

In this lesson, you will learn how to optimize machine learning models using Amazon SageMaker's Automatic Model Tuning or AMT for hyperparameter tuning. You will work with SageMaker to automatically find the best combination of hyperparameters to improve model performance.

Learning Objectives

By the end of this lesson, you will be able to:

• Set up and configure hyperparameter tuning jobs in Amazon SageMaker

• Identify key hyperparameters for optimizing machine learning models

• Understand how SageMaker’s Automatic Model Tuning (AMT) improves model performance

• Monitor tuning jobs and interpret results to select the best-performing model

Intended Audience

This lesson is designed for data scientists and developers interested in learning how to optimize machine learning models using Amazon SageMaker's hyperparameter tuning features.

Prerequisites

To get the most out of this lesson, you should have a basic understanding of machine learning concepts, AWS cloud services, and Amazon SageMaker. Experience with training and deploying ML models will be beneficial.