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.
Optimizing ML Systems for Real-World Deployment
Session Speakers
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Danielle Dean
Technical Director of Machine Learning, iRobot
Danielle Dean, PhD is the Technical Director of Machine Learning at iRobot where she is helping lead the intelligence revolution for robots. She leads a team that leverages machine learning, reinforcement learning, and software engineering to build algorithms that result in delightful user experiences. Before iRobot, Danielle was a Principal Data Scientist Lead at Microsoft within the Cloud AI Platform division. There, she led an international team of data scientists and engineers to build predictive analytics and machine learning solutions with external companies utilizing Microsoft's Cloud AI Platform. Danielle completed her Ph.D. in quantitative psychology with a concentration in biostatistics at the University of North Carolina at Chapel Hill.
