Feature Store Summit 2023

Check out all videos and slides presented at the conference!

From building to using Feature Stores for ML systems

Fabio shows how to leverage the feature, training and inference pipelines paradigm to develop and productionize ML systems.

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Palette Metadata Journey

The Uber team present their Palette Metadata Journey and how they have evolved the Metadata systems to meet Uber’s business needs.

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WeChat's Feature Platform For Real-Time Recommender Systems

Jin introduces WeChat's feature platform, emphasizing on their Apache Arrow based vectorized feature compute engine.

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Programmatic Feature Definition and Maintenance with Wayfair’s Mercury Feature Platform

Alexander talks about Mercury, Wayfair's feature platfrom and discuss how it facilitated maintenance of their ML systems.

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Driving Efficiency and Quality through a Feature Store at Delivery Hero

Breno talks about Delivery Hero's feature store platform and how it fosters standardization, feature reuse, and streamlined workflows.

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Stop Blaming the Algorithm: It’s Your Feature Store! - A guide to Monitoring your Feature Store

Claire elaborates on proactive issue detection in a feature store and model infrastructure to optimize model performance.

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Bringing ML Online: Defining ML Features in Real-Time with Bytewax

Zander discusses the vital but often neglected aspect of real-time ML systems and how Bytewax simplifies real-time feature pipelines.

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Feature Engineering with Hamilton: Write Once Run Everywhere

Elijah talks about Hamilton, a lightweight open-source framework in python enabling data practitioners to build & scale feature pipelines.

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Delivering Personalized & Realtime Context for LLM: Databricks Feature & Function Serving

The Databricks team demonstrates how to harness Databricks Feature and Function Serving with data in your Lakehouse for real-time context ingestion and retrieval.

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Feature Stores aren't about Storage

Simba breaks down the different types of feature store architectures, focusing on virtual architecture and its role in the MLOps stack.

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Architecture of Fennel’s realtime feature platform

Nikhil unveils Fennel's architecture, highlighting design choices, tradeoffs, and the impressive capabilities the platform has to offer.

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Uber's Risk Knowledge Platform

The Uber team showcases the scale and architecture of their risk knowledge platfrom and the team's custom solutions in addressing complex challenges.

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Accelerating Data Science through Feature Platforms and Generative AI

Xavier explores how bringing Feature Platforms and GenAI can make the entire feature engineering lifecycle more efficient.

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Operationalizing AI at Scale with H2O Feature Store

Learn about the origins and motivations behind AT&T and H2O.ai's Feature Store as well as key concepts and capabilities.

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