hands-on lab
Accelerating SageMaker Training With HyperParameter Tuning
Difficulty: Beginner
Duration: Up to 1 hour and 30 minutes
Students: 13
Get guided in a real environmentPractice with a step-by-step scenario in a real, provisioned environment.
Learn and validateUse validations to check your solutions every step of the way.
See resultsTrack your knowledge and monitor your progress.
Description
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:
Environment before
Environment after
Covered topics
Lab steps
Logging In to the Amazon Web Services Console
Opening JupyterLab on Your SageMaker Notebook
Training a Model With HyperParameter Tuning Jobs
Observing the HyperParameter Tuning Job