Using feature stores to deliver awesome models

In today’s fast-paced machine learning environment, the ability to efficiently manage and reuse features across multiple models is crucial. This workshop explores how leveraging a feature store can streamline ML pipelines by ensuring consistency and accelerating deployment cycles. Participants will gain hands-on experience with setting up, managing, and integrating feature stores into their existing workflows—transforming raw data into valuable, production-ready features.

Experience in model training, basic SQL knowledge, basic K8s knowledge

Join us for an immersive, hands-on session designed for data scientists, ML engineers, and AI enthusiasts eager to optimize their machine learning pipelines through advanced feature store functionality. In this workshop, we will focus on the robust capabilities of an open-source feature store platform Feast that streamlines the entire feature lifecycle without requiring you to reinvent the wheel.

What to Expect: • Unified Data Management: Learn how to consolidate offline and online feature data into a single, cohesive system. Discover strategies to ensure that the same high-quality features used during training are available during inference, eliminating training-serving skew and improving model consistency. • Scalable Feature Engineering: Explore methods to automate data transformations and store reusable components. This session will show you how to reduce redundancy and accelerate model iteration by centralizing your feature definitions. • Hands-On Integration: You will learn how to deploy Feast feature store into Kubernetes.

By the end of the workshop, you’ll have practical experience and actionable insights to implement a feature store that elevates your machine learning workflows in Kubernetes environment.