Streamlining Amazon SageMaker Governance With Model Cards

Machine learning models are growing in importance to businesses and organizations as they can be key to generating value for customers. Amazon SageMaker, in addition to enabling you to train and develop machine learning models, also support features that help you govern and audit your machine learning models.

Learning how to create a model card in Amazon SageMaker will benefit anyone looking to responsibly create and use machine learning models in a production environment.

In this hands-on lab, you will create, inspect, and export an Amazon SageMaker Model card.

Learning objectives

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

• Use a Jupyter Lab notebook
• Programmatically create a new model card
• Export a model card to a PDF
• Modify the status of a model card

Intended audience

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

Prerequisites

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

• Amazon SageMaker
• Jupyter Lab
• The Python scripting language

The following content can be used to fulfill the prerequisites:

• Amazon SageMaker - ML Governance
• Understanding Amazon SageMaker Machine Learning Environments
• Python for Beginners

Updates

Mar 7th, 2025 - Updated the instructions and screenshots to reflect the latest UI