Agenda 2023

Kick-off

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Jim Dowling

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Jim Dowling will kick off the event with a retrospective analysis of the feature store landscape in 2023.

From building to using feature stores for ML systems

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

Palette Metadata Journey

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Paarth Chothani

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Staff Software Engineer

Staff Software Engineer

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Uber

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

WeChat's Feature Platform For Real-Time Recommender Systems

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

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

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

The Logical Feature Store: Managing Data for ML

Georgia will introduce the logical feature store, its data management capabilities, and organizational design patterns to support them.

Driving Efficiency and Quality through a Feature Store at Delivery Hero

Breno talks about their feature store platform and how it fosters standardization, feature reuse, and streamlined workflows.

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

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Claire Longo

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Head of ML Solutions Engineering

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Arize

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

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

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

Feature Engineering with Hamilton: Wrote Once Run Everywhere

Elijah will discuss Hamilton, a lightweight open-source framework in python that enables data practitioners to cleanly define dataflows.

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

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

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.

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.

Uber's Risk Knowledge Platform

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Christopher Settles

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Machine Learning Engineer

Senior Software Engineer

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

Accelerating Data Science through Feature Platforms and Generative AI

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

Wrap-up

Jim Dowling wraps up the event.

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More Information

Agenda

Kick-off

From building to using feature stores for ML systems

Palette Metadata Journey

WeChat's Feature Platform For Real-Time Recommender Systems

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

The Logical Feature Store: Managing Data for ML

Driving Efficiency and Quality through a Feature Store at Delivery Hero

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

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

Feature Engineering with Hamilton: Wrote Once Run Everywhere

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

Feature Stores aren't about storage

Architecture of Fennel’s realtime feature platform

Uber's Risk Knowledge Platform

Accelerating Data Science through Feature Platforms and Generative AI

Wrap-up

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