Explore our agenda below!
Jim Dowling will talk about the featurestore.org community and kickstart the event.
Amit will show how the Michelangelo ML Platform uses the Palette Feature Store to address inefficiencies of the model lifecycle.
Hopsworks will share the lessons learned over the past four years of building their platform and what lies ahead.
Brian will go over Doordash's feature store architecture, learnings from supporting CRDB and what it takes to build a feature store.
David and Xiaoyong will cover the background of Feathr, its core concepts and design, and their journey on scaling an enterprise FS.
A discussion on the historical evolution and the future of APIs for feature stores.
Dustin will describe his team's journey towards building Nexus, an in-house feature store and how it accelerates feature engineering.
Nikhil will introduce Zipline, a declarative feature engineering platform developed at Airbnb, which will be open-sourced in March.
Sinan will cover how Shiba uses a Feature Store to maintain ML models with up-to-date data on how bad actors target communities.
Aman will discuss the state of ML production monitoring, its challenges, and how to actively improve models and features in production.
A discussion on challenges of data quality for features and whether differentiate from general data quality requirements for analytics.
Stefan will present Hamilton an open source Python micro framework that solved his team's pain points by changing their working paradigm.
Richard will elaborate on how AFCU's adoption of the Hopsworks Feature Store has helped them significantly improve their workflows.
Achnit will talk about Sub-ML, a class of applications simpler than traditional ML approaches and often used in decision support systems.
Simba from Featureform will share the team's learnings from different companies on usage patterns of feature stores.
A discussion on the challenges of making the feature store disappear and become part of the workflow of data science and data engineering.
Gaurav will focus on how enterprises can apply the power of the semantic layer to enrich feature stores and scale business-ready AI.
Ryohei from dotData will discuss how Feature Discovery and Feature Factory concepts can transform your feature development process.
Ravi will discuss how Dagger can be used with feature stores to empower data scientists to make feature engineering self-service.
Greg from Sumatra will discuss feature designs and describe their journey developing a DSL for streaming feature transformation.
Sarah, PhD will introduce the notion of feature store regret that helps evaluate feature quality of different maintenance policies.
Lu introduces OpenMLDB, an open-source ML database that provides a real-time feature platform for ML applications that reduces dev cost.