Sergey Ermolin
Principal Solutions Architect, ML/DL/AI, Amazon Web Services

Sergey Ermolin is a principal solutions architect (ML/DL/AI) for Amazon Web Services. Previously, he was a software solutions architect for deep learning, Spark analytics, and big data technologies at Intel. A Silicon Valley veteran with a passion for machine learning and artificial intelligence, Sergey has been interested in neural networks since 1996, when he used them to predict aging behavior of quartz crystals and cesium atomic clocks made by Hewlett-Packard. Sergey holds an MSEE and a certificate in mining massive datasets from Stanford and BS degrees in both physics and mechanical engineering from California State University, Sacramento.

Sessions

13:30–17:00 Tuesday, 15 October 2019

Location: Windsor Suite

Using reinforcement learning to build recommendation systems with AWS SageMaker RL

Secondary topics: Machine Learning, Machine Learning tools, Reinforcement Learning

Sergey Ermolin (Amazon Web Services), Vineet Khare (Amazon Web Services)

Average rating:

(1.50, 4 ratings)

Sergey Ermolin and Vineet Khare provide a step-by-step overview on how to implement, train, and deploy a reinforcement learning (RL)-based recommender system with real-time multivariate optimization. They show you how leverage RL to implement a recommender system that optimizes an advertisement message that promotes adoption of merchant's services. Read more.

14:35–15:15 Wednesday, 16 October 2019

Location: Park Suite

Build, train, and deploy predictive maintenance models at industrial scale (sponsored by AWS)