Companies are increasingly investing in Machine Learning (ML) to deliver new customer experiences and re-invent business processes. Unfortunately, the majority of operational ML projects never make it to production. The most significant blocker is the lack of infrastructure and tooling required to build production-ready data for ML.

Kevin Stumpf has a long history of building data infrastructure for ML, first for Uber Michelangelo, and most recently as co-founder of Tecton. Kevin will share his insights on the challenges of getting ML features to production. We’ll discuss the role of the feature store in bringing DevOps-like efficiency to building ML features. Kevin will also provide an overview of Tecton, which aims to bring an enterprise-grade feature store to every company.

Kevin co-founded Tecton where he leads a world-class engineering team that is building a next-generation feature store for operational Machine Learning. Kevin and his co-founders built deep expertise in operational ML platforms while at Uber, where they created the Michelangelo platform that enabled Uber to scale from 0 to 1000's of ML-driven applications in just a few years. Prior to Uber, Kevin founded Dispatcher, with the vision to build the Uber for long-haul trucking. Kevin holds an MBA from Stanford University and a Bachelor's Degree in Computer and Management Sciences from the University of Hagen. Outside of work, Kevin is a passionate long-distance endurance athlete.

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Connect with Kevin on LinkedIn: https://www.linkedin.com/in/kevinstumpf/

MLOps #22 Feature Stores: An Essential Part of the ML Stack to Build Great Data // Kevin Stumpf - Co-Founder & CTO at Tecton 01:05:54 Share

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MLOps #22 Feature Stores: An Essential Part of the ML Stack to Build Great Data // Kevin Stumpf - Co-Founder & CTO at Tecton