A Data Processing Pipeline to Enable Machine Learning
This project focuses on the development of a reproducible data preparation pipeline that builds a traceable and accessible database of imagery for use in downstream machine learning (ML) and data science applications. Datasets available in the ML literature are often difficult to access and explore. Building traceable, accessible data pipelines accelerates the development of ML applications by enabling the rapid access to data.
Intern: Nina Whale
Mentors: Matthew Abernathy and Alvin Isaac Morgan (AMDS)