Analyzing Model Performance with Amazon SageMaker

Welcome to this lesson which will explain how to analyze machine learning model performance using Amazon SageMaker.

Learning objectives:

By the end of this lesson, you will have a greater understanding of Amazon SageMaker, including:

• Model evaluation techniques and the different model metrics

• How to evaluate foundation model performance in Amazon SageMaker

• Model convergence issues and how to identify them with Amazon SageMaker Debugger

• How to gain insights into model performance and bias with Amazon SageMaker Clarify

Prerequisites:

• To get the most out of this lesson, you should have a basic understanding of Amazon SageMaker. It also helps to have general machine learning knowledge. For more information about these services, see our existing content titled: Introduction to Amazon SageMaker https://platform.qa.com/course/introduction-to-sagemaker-1200/?context_resource=lp&context_id=453

Intended audience:

• This lesson has been created for those who are interested in the performance of machine learning models. This may include machine learning engineers, software engineers, data scientists or users looking to achieve the AWS Certified AI Practitioner certification or the AWS Certified Machine Learning Engineer - Associate certification.