Online A/B Testing of Real-Time Event Detection Systems - David Tagliamonti | Stanford MLSys #93

Episode 93 of the Stanford MLSys Seminar Series!
Online A/B Testing of Real-Time Event Detection Systems
Speaker: David Tagliamonti
Abstract:
AI models increasingly impact our day-to-day lives. For teams building AI-powered products, the first version of a model is only the first step on a long journey. The ability to quickly iterate on models is key to fast product development and better user experience. A/B testing is one way to test new models before rolling them out. In this talk, we discuss the unique challenges of A/B testing real-time event detection systems, particularly in mission-critical environments, and a specific approach to doing so. Finally, we reflect on our experience using this approach in production for several years.
Bio:
David is a Staff Software Engineer at Ambient AI, where he leads the Forensics product, an AI-powered video investigation platform. He previously held roles as an Applied Research Scientist and later Senior Research Scientist, where he worked on applying the latest advances in Deep Learning for Computer Vision to Ambient's perception platform. Prior to that, David earned an MS in Computer Science from Stanford University and a BS in Actuarial Mathematics from Concordia University in Montreal, Canada.
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Stanford MLSys Seminar hosts: Avanika Narayan, Benjamin Spector, Michael Zhang
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