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: Participate in live coding sessions where you’ll integrate the feature store into your ML pipeline. • Enhanced Collaboration and Governance: Discover how a centralized feature repository fosters better teamwork among data professionals, enhances reproducibility, and supports comprehensive data governance practices throughout your ML projects.
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.
Laurynas is a machine learning engineer who has been working in the field for the last 10 years focusing on different data problems in verticals of telecommunications, human resources, cybersecurity and devops. He has great passion in automating ML pipelines and is a strong believer in the value of data. Most recently, he has been working in CAST AI where he spent great deal of time of making Kubernetes automation more intelligent using various methods of machine learning.
Mantas Čepulkovskis is a data scientist specializing in applications of machine learning. He has held senior roles at CAST AI, IBM, and Danske Bank, developing ML solutions across various industries. He earned an MSc in Physics from the University of Copenhagen, specializing in theoretical quantum physics.