In the real world, machine learning models are components of a broader software application or system. In this talk, Danielle Dean of iRobot will explore the importance of optimizing the system as a whole–not just optimizing individual ML models. Based on experience building and deploying deep-learning-based systems for one of the largest fleets of autonomous robots in the world (the Roomba!), Danielle will highlight critical areas requiring attention for system-level optimization, including data collection, data processing, model building, system application and testing. She will also share recommendations for ways to think about and achieve optimization of the whole system.