Build, train, and deploy predictive maintenance models at industrial scale (sponsored by AWS)
Sunil Mallya walks you through building complex ML-enabled products using RL, explores hardware design challenges and trade-offs, and details real-life examples of how any developer can up-level their RL skills through autonomous driving.
This session is sponsored by AWS.
What you'll learn
- Learn how to build complex products using RL
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.
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