Sensor based statistical models promise to support a variety of advances in human computer interaction, but building applications that use them is currently difficult and potential advances go unexplored. We present Subtle, a toolkit that removes some of the obstacles to developing and deploying applications using sensor based statistical models of human situations. Subtle provides an appropriate and extensible sensing library, continuous learning of personalized models, fully automated high level feature generation, and support for using learned models in deployed applications. By removing obstacles to developing and deploying sensor based statistical models, Subtle makes it easier to explore the design space surrounding sensor based statistical models of human situations. Subtle thus helps to move the focus of human computer interaction research onto applications and datasets, instead of the difficulties of developing and deploying sensor based statistical models.