Meetup #22

Feature Stores: An Essential Part of the ML Stack to Build Great Data

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.

In this episode

Kevin  Stumpf

Kevin Stumpf

Co-Founder & CTO, Tecton

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.

@kevinmstumpf

LinkedIn

Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.