Co-Founder and 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.
Watch live: March 2 | 4.30PM ET
How Feature Stores Enable Operational ML
Getting Machine Learning applications into production is hard. When those applications are core to the business and need to run in real-time, the challenge becomes even harder. Feature Stores are designed to solve the data engineering challenges of production ML applications, tackling four key problems:
1. Real-time and streaming data are difficult to incorporate into ML models
2. ML teams are stuck building complex data pipelines
3. Feature engineering is duplicated across the organization
4. Data issues break models in production