Closing the Production Gap with MLOps

Abstract: 

This session will explore and demonstrate how DataRobot's MLOps can speed up deployment, monitor drift and accuracy, ensure governance and ongoing model lifecycle management, including how to do automation retraining and have challenger models in production. Also, this session will cover how to deploy and monitor models built outside of DataRobot.

Bio: 

Pavel Ustinov is a Lead AI Engineer at DataRobot, where he applies his scientific background and more than 15 years' experience in software engineering to successfully deliver end-to-end Augmented Intelligence solutions driven by the DataRobot platform. He takes care of EMEA region customers. Pavel began his career as a researcher in the nonlinear dynamics of energy conversion systems before moving to the software engineering and machine learning industry. Prior to joining DataRobot, he also spent time at Anaplan, Société Générale CIB, BNP Paribas Securities Services, and ML/DL startups holding senior/leading technical positions and delivering large scale analytical projects. Pavel received his PhD from Orel State Technical University (Orel, Russia) in Automation and Control of Industrial Systems and PhD from University of Reims Champagne-Ardenne (Reims, France) in Computer Science, Automation and Signal Processing.
When not working out over technology he enjoys a high altitude mountaineering.

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