In this webinar on Feature Stores for Accelerating AI Development we’re joined by leaders from Tecton, Gojek, and Preset to discuss how organizations can increase value and decrease time-to-market for machine learning using feature stores, MLOps, and open source.
We discuss the main data challenges of AI/ML, and the role of the feature store in solving those challenges.
TWIML’s Sam Charrington hosted the discussion, with special guests:
Kevin Stumpf – Co-founder & CTO, Tecton
Kevin co-founded Tecton.AI where he leads a world-class engineering team that is building an enterprise feature store for 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 Science from the University of Hagen. Outside of work, Kevin is a passionate long-distance endurance athlete.
Willem Pienaar – Engineering Lead, Gojek, and founder of the Feast project
Willem Pienaar leads the Data Science Platform team at Gojek, developing the Gojek ML platform, which supports a wide variety of models and handles over 100 million orders every month. His main focus areas are building data and ML platforms, allowing organizations to scale machine learning and drive decision making. In a previous life, Willem founded and sold a networking startup.
Max Beauchemin – Founder & CEO, Preset
Max Beauchemin has worked at the leading edge of data and analytics his entire career, helping shape the discipline in influential roles at companies like Yahoo!, Facebook, Airbnb, and Lyft. A leader in the open- source community, Max is the original creator of Apache Airflow, and Apache Superset, the popular open source project underpinning his now startup Preset.
Deploying ML to production is hard, and data is often the hardest part.
Data scientists don’t always have access to the tooling required to deploy features to production quickly and reliably. This is where the feature store comes in. A feature store is a data platform that allows data scientists to build and deploy features within hours instead of months. Join us for this webinar to learn how.
