Sudeep Pillai

Sudeep Pillai

Team Lead, ML Engineering
Toyota Research Institute

Sudeep leads the ML engineering team at Toyota Research Institute (TRI) to build and deploy state-of-the-art computer vision and machine learning models to Toyota’s next-generation L4 autonomous vehicle fleet. At TRI, we’re committed to solving the hard automated driving problem, where we leverage Toyota’s unique data-advantage to truly unlock the value of structured data that autonomous vehicle fleets collect at scale. Previously, he was a research scientist in the ML team at TRI, spearheading their large-scale self-supervised learning efforts for autonomous driving. He received his PhD in Computer Science from MIT, where he focused on self-supervised perception and learning in spatially-cognizant mobile robots.

MLOps for High-Stakes Environments
Tuesday, January 19 | 
10:40 AM - 
11:10 AM

In this talk Sudeep will share an overview of the MLOps environment developed at TRI and discuss some of the key ways MLOps techniques must be adapted to meet the needs of high-stakes environments like robotics and autonomous vehicles. Environments like these add the challenges of edge or fleet deployment to those seen in traditional “online” environments, as well as the additional threat that if your model doesn’t perform well in production, people can get hurt. Attendees will learn how TRI has adapted MLOps techniques to achieve the desired level of efficiency while still ensuring model safety through a more rigorous approach to model testing, the use of open and closed loop checks, and the strategic use of a humans-in-the-loop for deployment to the fleet.

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