Model Evaluation with Amazon SageMaker
Machine learning models need to be high performing and able to generalize new data. Evaluating these models checks how capable they are of doing just that. This course will teach you how to evaluate machine learning models with Amazon SageMaker.
What you'll learn
Having and maintaining high performing machine learning models is the key to having successful AI solutions. In this course, Model Evaluation with Amazon SageMaker, you’ll learn to assess the performance of machine learning models. First, you’ll explore how to evaluate models using SageMaker Canvas, focussing on the key metrics.. Next, you’ll discover how to do a similar evaluation with the Python SDK for model evaluation. Finally, you’ll learn how to identify model drift and apply best practices for continuous performance improvement of your machine learning models.
When you’re finished with this course, you’ll have the skills and knowledge of model evaluation needed to make sure that your models are, and remain, effective in various AI applications.
Table of contents
About the author
Maaike is trainer and software developer. She founded training agency Brightboost in 2014 and spends most of her days and nights working and learning. Training gives her the opportunity to combine her love for software development with her passion to help others boost their careers and be successful. She has trained professionals in the field of Java, Spring, C#, Python, Scrum, React and Angular. A lot of her time is spend staying up-to-date with the latest developments in her field. Next to the... more