Amazon SageMaker Clarify is a new capability of Amazon SageMaker that helps customers detect bias in machine learning models, and increase transparency by helping explain model behavior to stakeholders and customers
In this first video, I show how you to use the bias detection capability in Amazon SageMaker Clarify, using bias metrics computed on a credit dataset, and on a classification model trained on this dataset.
In this second video, I show how you to use the model explainability capability in Amazon SageMaker Clarify, using SHAP values computed on a credit model.
About the Author
Julien Simon is the Chief Evangelist at Arcee AI
, specializing in Small Language Models and enterprise AI solutions. Recognized as the #1 AI Evangelist globally by AI Magazine in 2021, he brings over 30 years of technology leadership experience to his role.
With 650+ speaking engagements worldwide and 350+ technical blog posts, Julien is a leading voice in practical AI implementation, cost-effective AI solutions, and the democratization of artificial intelligence. His expertise spans open-source AI, Small Language Models, enterprise AI strategy, and edge computing optimization.
Previously serving as Principal Evangelist at AWS and Chief Evangelist at Hugging Face, Julien has authored books on Amazon SageMaker and contributed to the open-source AI ecosystem. His mission is to make AI accessible, understandable, and controllable for everyone.