Enhance the process of iterative machine learning model development by integrating features into existing feature groups within the Amazon SageMaker Feature Store.
By Samuel Parker, Emily Thompson, Jason Lee, and Priya Patel
Published on 01 AUG 2022
Categories: Advanced (300), Amazon Machine Learning, Amazon SageMaker, Artificial Intelligence, Technical How-to
Feature engineering presents one of the most complex challenges in the machine learning (ML) lifecycle, consuming a significant portion of the time data scientists and ML engineers dedicate to their projects—approximately 60-70% is spent on feature engineering. AWS launched Amazon SageMaker Feature Store during AWS re:Invent 2020 to address this issue. This service is a fully managed, centralized tool designed specifically for feature storage and management. For more insights on feature engineering, check out another blog post here, which discusses related concepts.
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