Jim Dowling will kick off the event with a retrospective analysis of the feature store landscape in 2023.
Learn how to leverage the feature, training and inference pipelines paradigm to develop and productionize ML systems.
The Uber team will present their Palette Metadata Journey and how they have evolved the Metadata systems to meet Uber’s business needs.
Jin will introduce WeChat's feature platform, emphasizing on their Apache Arrow based vectorized feature compute engine.
Alexander will talk about Mercury, Wayfair's feature platfrom and discuss how it facilitated maintenance of their ML systems.
Georgia will introduce the logical feature store, its data management capabilities, and organizational design patterns to support them.
Breno talks about their feature store platform and how it fosters standardization, feature reuse, and streamlined workflows.
Claire will elaborate on proactive issue detection in a feature store and model infrastructure to optimize model performance.
Zander will discuss the vital but often neglected aspect of real-time ML systems and how Bytewax simplifies real-time feature pipelines.
Elijah will discuss Hamilton, a lightweight open-source framework in python that enables data practitioners to cleanly define dataflows.
Discover how to harness Databricks Feature and Function Serving with data in your Lakehouse for real-time context ingestion and retrieval.
Simba breaks down the different types of feature store architectures, focusing on virtual architecture and its role in the MLOps stack.
Nikhil unveils Fennel's architecture, highlighting design choices, tradeoffs, and the impressive capabilities the platform has to offer.
Explore the scale and architecture of Uber's risk knowledge platfrom and the team's custom solutions in addressing complex challenges.
Xavier will explore how bringing Feature Platforms and GenAI can make the entire feature engineering lifecycle more efficient.