How Enterprises Succeed with MLOps at Scale


Machine learning and AI are a piece of a larger system. In order to run MLOps at scale, you need people and technology to work together to create repeatable, scalable and reproducible processes. We will discuss how to achieve scale, avoid common problems and look at use cases from major industry players.


David has over 20 years of experience in the fields of data, AI and enterprise cloud. He has led teams for EMC Dell, Hitachi and Cisco, working with some of the most innovative companies in the world in both classified and commercial environments. Today, David acts as the Western Regional Director at Iguazio, working with Enterprise customers to help them bring their data science initiatives to life. David is passionate about applying MLOps principles to real-world AI projects, on-premise, in multi-cloud environments, on a SCIF or all of the above. When he’s not working with customers on AI projects, he volunteers at the Salvation Army and Rotary International. He and his wife have twins - a boy and a girl, as well as a 94lb/43kg Labrador that eats everything.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

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