The fast-paced nature of machine learning research and development, with many ideas advancing rapidly from research to production, puts it at increased risk of programming errors, which can be particularly insidious when combined with machine learning. In this talk we discuss defensive design as a way to reduce the chance for such errors to occur in the first place, and present AutoGraph, a tool which facilitates defensive design by allowing more legible code that is still efficient and portable.