In research and industry, it is often acknowledged that the main bottleneck in machine learning adoption is creating sufficiently large labeled data sets. To address this issue, we introduce SMART, an open source web application designed to help teams efficiently build labeled training data sets for supervised machine learning tasks. SMART provides users with an intuitive interface for creating labeled data sets, supports active learning to help reduce the required amount of labeled data, and incorporates inter-rater reliability statistics to provide insight into label quality. The project website contains links to the code repository and extensive user documentation https://rtiinternational.github.io/SMART/. Robert Chew, Michael Wenger, Caroline Kery, Jason Nance, Keith Richards, Emily Hadley and Peter Baumgartner