Accelerating SageMaker Training With HyperParameter Tuning

Amazon SageMaker enables you to build, train, and deploy machine learning models in the public AWS cloud. HyperParameter tuning is a form of Automatic Model Tuning that helps reduce the manual effort of training and optimizing models.

Learning how to use HyperParameter tuning will benefit anyone looking to build and train machine learning models in Amazon SageMaker.

In this hands-on lab, you will use a JupyterLab notebook to prepare a dataset and train a model using a HyperParameter Tuning Job.

Learning objectives

Upon completion of this beginner-level lab, you will be able to:

• Access a JupyterLab notebook in Amazon SageMaker
• Prepare a dataset for training a machine learning model
• Launch a HyperParameter Tuning Job to find the best hyperparameters for a model
• Observe the progress of a HyperParameter Tuning Job

Intended audience

• Candidates for the AWS Certified Machine Learning Engineer Associate certification
• Cloud Architects
• Data Engineers
• Machine Learning Engineers
• Software Engineers

Prerequisites

Familiarity with the following will be beneficial but is not required:

• Amazon SageMaker
• Amazon S3
• The Python programming language

The following content can be used to fulfill the prerequisites:

• Amazon SageMaker: Machine Learning Workflows
• Introduction to Amazon S3
• Python for Beginners