One rising challenge in ML is how can we manage and serve features at scale, enabling data scientists and engineers to efficiently create, store, and share features across different stages of the machine learning pipeline.

In this session, we will delve into the world of Feature Stores and their emerging role in MLOps. We will explore important concepts including feature engineering, feature versioning, feature serving, and feature metadata management. With practical demos in two leading Feature Store implementations, Databricks and Feast, we will explore the benefits, best practices, common challenges and pitfalls and how to address them.

Whether you are a data scientist, machine learning engineer, or data engineer, this session will provide valuable insights and practical demos to help you harness the power of Feature Stores in your organisation's MLOps journey.