Responsible AI with Amazon SageMaker AI

This course covers key topics such as detecting bias in datasets and models, explaining model predictions, and integrating responsible AI practices into your ML workflows using Amazon SageMaker Clarify. Gain hands-on experience with SageMaker's governance tools, including Role Manager, Model Cards, and Model Dashboard, to manage permissions, document models, and monitor performance. By the end of this course, you'll be equipped with the skills you need to build fairer, more explainable AI models and ensure compliance with ethical standards in AI. Whether you’re a data scientist, ML engineer, or technical leader, instructor Kesha Williams covers the essentials of practicing responsible AI in your organization.

Learning objectives
Identify and evaluate bias in machine learning datasets and models using Amazon SageMaker Clarify.
Generate and interpret model explanations to ensure transparency and understandability of AI models.
Integrate SageMaker Clarify with SageMaker Autopilot to enhance model explainability and fairness in automated workflows.
Implement ML governance practices, including managing permissions, creating model documentation, and monitoring model performance using SageMaker AI tools.