Agenda 2025

Kick - Off

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Jim Dowling

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Jim Dowling kicks off the event with insights on how feature stores are transforming Real-Time AI.

From Real-Time ML to Agents with Hopsworks

Learn to build batch, real-time, and agentic AI systems using a unified FTI pipeline architecture.

Lyft’s Feature Store: Architecture, Optimization, and Evolution.

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Rohan Varshney

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Senior Software Engineer

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Lyft

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Learn about Lyft’s Feature Store: its architecture, key use cases, performance, and evolution over 5+ years.

Powering Real-Time AI at Pinterest: Feature Management at Scale with Galaxy Signal Platform.

Learn about Pinterest’s Galaxy Platform: architecture, real-time feature serving, and lessons at scale.

Vector Store: Uber’s Embedding Platform

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Divya Nagar

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Staff Software Engineer

Learn how Uber’s Vector Store powers embeddings at scale for GenAI, RAG, semantic search, and predictive models.

Predictive Analytics in Financial Industry

Learn how AI and predictive analytics are transforming finance, boosting revenue, and improving customer experience.

Real time ML at Roku

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Krishna Chaitanya Chakka

Learn how Roku scales real-time feature serving and speeds ML model development with Chronon

Bridging Real-Time and Batch: Declarative Feature Engineering with Apache Hamilton + Narwhals

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Ryan Whitten

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Director, ML Data Engineering

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Best Egg

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Learn how Apache Hamilton and Narwhals unify real-time and batch feature generation for reliable, low-latency AI.

How Coinbase Builds Sequence Features for Machine Learning

Learn how Coinbase deploy user action sequences at scale to improve ML model performance.

Real-time ML: Accelerating Python for inference (< 10ms) at scale

Learn how a symbolic Python interpreter converts Python into DAGs to accelerate ML pipelines.

Real-Time Feature Aggregation at Scale: iFood’s Path to Sub-Second Latency

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Willian Moreira

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Machine Learning Platform Lead

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iFood

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Learn how iFood built a sub-second feature platform with Spark and Redis to power real-time ML pipelines.

Building a Generative Recommender with Chronon

Learn how Chronon powers scalable, low-latency feature pipelines for next-gen recommendation systems.

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More Information

Agenda

Kick - Off

From Real-Time ML to Agents with Hopsworks

Lyft’s Feature Store: Architecture, Optimization, and Evolution.

Powering Real-Time AI at Pinterest: Feature Management at Scale with Galaxy Signal Platform.

Vector Store: Uber’s Embedding Platform

Predictive Analytics in Financial Industry

Real time ML at Roku

Bridging Real-Time and Batch: Declarative Feature Engineering with Apache Hamilton + Narwhals

How Coinbase Builds Sequence Features for Machine Learning

Real-time ML: Accelerating Python for inference (< 10ms) at scale

Real-Time Feature Aggregation at Scale: iFood’s Path to Sub-Second Latency

Building a Generative Recommender with Chronon

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