RL in production: A roundtable discussion with industry leaders

Paige Bailey is the developer experience and product lead for Ray and its open-source ecosystem. Prior to joining Anyscale, Paige was director of machine learning and MLOps at GitHub; lead PM for machine learning frameworks at Google Brain and DeepMind; and a senior software engineer at Microsoft. She has over a decade of experience as a machine learning practitioner, and cares deeply about reducing the friction for bringing large-scale models into production. You can find her on GitHub and Twitter at @dynamicwebpaige.

Richard Liaw is an Engineering Manager at Anyscale, where he leads a team in building open source libraries on top of Ray. He is on leave from the PhD program at UC Berkeley, where he worked at the RISELab advised by Ion Stoica, Joseph Gonzalez, and Ken Goldberg. In his time in the PhD program, he was part of the Ray team, building scalable ML libraries on top of Ray.

Adam Kelloway is wearing many hats (Data Sci, ML Eng. & Data Eng.) for Dow’s Digital Fulfillment Center as he shapes, grows, and matures Dow’s AI/ML/Data Strategies. When he’s building models those are focused on Dow’s Supply Chain operations. He is driving the adoption of RL, ML, and MIP based agents to accomplish Dow’s multi-agent automated and intelligent digital supply chain. Recent highlights include leveraging Ray Core and Ray Serve to distribute and deploy a hybrid Simulation/Mixed Integer Model for production planning and continued work to productionize RL agents for production scheduling.

Adam has a Master of Engineering (MEng) degree from Imperial College London and a Ph.D. in Chemical Engineering from the University of Minnesota where he focused on the application of process modeling and optimization to the technical and economic feasibility analyses of biomass-based process technologies. He is a member of AICHE and represents Dow on the Manufacturing Leadership Council. He has presented work at numerous conferences including AICHE, Aspen Optimize, Ray Summit, and NeurIPS.

Adam is married to Claire, a physical therapist specializing in neurological rehab, and lives in Houston with their dog Chester.

As a dedicated applied researcher and physicist I started my scientific career at the international Dark Matter search experiment XENON. After Postdoc positions at MPIK Heidelberg and Columbia University in N.Y.C, I changed fields to join the Learning Systems groups at Siemens Technology where I have been driving applied machine learning topics and solving control optimization problems for the industrial domain using AI and reinforcement learning.

Volkmar Sterzing is over 30 years active in the Neural Network and Machine Learning field. Together with his research team at Siemens, he pioneered forecasting and industrial control applications of AI and reinforcement learning in various applications. In 2017 he was awarded inventor of the year at Siemens for the reinforcement learning based continuous gas turbine tuning. This application has now become a product of Siemens Energy. Volkmar heads the Research Group "Learning Systems" at Siemens Technology.

Ben Kasper is a Staff Data Scientist at Riot Games, where he works on an applied AI research team with a focus on deep reinforcement learning. Before that, he helped deliver first-of-their-kind applications of machine learning in the games industry, including the detection and penalization of unsportsmanlike behavior in League of Legends and preemptive game balance in Legends of Runeterra. He holds an MS in Statistics from Stanford University.