Amit Nene from Uber shows how the Michelangelo ML Platform uses the Palette Feature Store to addresses inefficiencies of the model lifecycle.
Get the presentationMoritz Meister and Fabio Buso from Hopsworks share the lessons learned over the past four years of building their platform and what lies ahead.
Get the presentationBrian Seo goes over Doordash's feature store architecture, learnings from supporting CRDB and what it takes to build a feature store.
Get the presentationDavid Stein from Linkedin and Xiaoyong Zhu from Microsoft cover the background of Feathr, its core concepts and design, and their journey on scaling an enterprise FS.
Get the presentationModerated by Jim Dowling from Hopsworks. A discussion on the historical evolution and the future of APIs for feature stores.
Get the presentationDustin Hamerla from Disney Streaming describes his team's journey towards building Nexus, an in-house feature store and how it accelerates feature engineering.
Get the presentationNikhil Simha from Airbnb introduces Zipline, a declarative feature engineering platform developed at Airbnb, which will be open-sourced in March.
Get the presentationSinan Ozdemir covers how Shiba uses a Feature Store to maintain ML models with up-to-date data on how bad actors target communities.
Get the presentationAman Khan from Arize discusses the state of ML production monitoring, its challenges, and how to actively improve models and features in production.
Get the presentationModerated by Patrik Liu Tran from Validio. A discussion on challenges of data quality for features and whether differentiate from general data quality requirements for analytics.
Get the presentationStefan Krawczyk from Stitch Fix presents Hamilton an open source Python micro framework that solved his team's pain points by changing their working paradigm.
Get the presentationRichard Woolston elaborates on how AFCU's adoption of the Hopsworks Feature Store has helped them significantly improve their workflows.
Get the presentationAchnit Thomas from Scribble Data talks about Sub-ML, a class of applications simpler than traditional ML approaches and often used in decision support systems.
Get the presentationSimba Khadder from Featureform shares the team's learnings from different companies on usage patterns of feature stores.
Get the presentationModerated by Sarah Catanzaro from Amplify Partners. A discussion on the challenges of making the feature store disappear and become part of the workflow of data science and data engineering.
Get the presentationGaurav Rao from AtScale talks about how enterprises can apply the power of the semantic layer to enrich feature stores and scale business-ready AI.
Get the presentationLulu Liu from dotData will discuss how Feature Discovery and Feature Factory concepts can transform your feature development process.
Get the presentationRavi Suhag from Open DataOps Foundation talks about Dagger and hot it can be used with feature stores to empower data scientists to make feature engineering self-service.
Get the presentationGreg Kuhlmann from Sumatra discusses feature designs and describe their journey developing a DSL for streaming feature transformation.
Get the presentationSarah Wooders, PhD UC Berkeley introduces the notion of feature store regret that helps evaluate feature quality of different maintenance policies.
Get the presentationLu Mian from 4Paradigm introduces OpenMLDB, an open-source ML database that provides a real-time feature platform for ML applications that reduces dev cost.
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