
Amy Nguyen
Head of DevRel
Chalk
Andrew Hwang
Staff Machine Learning Engineer


Arjit Jain
CTO & Co-Founder
TurboML
Caio Camatta
Software Engineer


Chunhao Zhang
Senior Software Engineer


Dhruva Dixith Kurra
Senior Machine Learning Engineer


Divya Manohar
Software Engineer


Eduards Sidorovics
Senior Software Engineer


Elliot Marx
Co-Founder
Chalk
Harish Nagu Sana
Staff Software Engineer

Javier de la Rúa Martínez
Research Engineer


Jim Dowling
CEO & Co-Founder


Laura Funderburk
Senior Developer Advocate

Nicholas Marcott
Staff Software Engineer


Nikhil Garg
Co-Founder & CEO
Fennel
Paarth Chothani
Staff Software Engineer


Pengyu Hou
Senior Software Engineer


Prachi Poddar
ML Engineering Manager


Raymond Cunningham
Engineering Manager


Supriya Raman
AI Engineering Manager - WatsonX Client Engineering
IBM
Tianne Cui
Senior Software Engineer


Tun Shwe
VP of Data
Quix
Vijay Kulkarni
Engineering Manager

Vishakha Sharma
Senior Principal Data Scientist
Roche Diagnostics
Zander Matheson
CEO & Founder
- Oct 15, 06:30 AM UTC · 10 min · Kickoff
› Beyond Feature Stores: Data for AI in Real-time, Batch, and LLMs
We will explore the journey from traditional Feature Stores to a more comprehensive framework that integrates these diverse data types.
- Oct 15, 06:40 AM UTC · 20 min · Presentation
› From Feature Store to AI Lakehouse
Hopsworks introduces the AI Lakehouse - an extension to the Lakehouse with MLOps capabilities, real-time data support, LLMs and more.
- Oct 15, 07:00 AM UTC · 20 min · Presentation
› Uber's GenAI Oncall Co-Pilot Journey
Uber discusses how they built a GenAI Bot to boost on-call efficiency and minimize downtime.
- Oct 15, 07:20 AM UTC · 20 min · Presentation
› Chronon, Airbnb's Open Source Feature Engineering Framework
Airbnb talks about Chronon - an open-source solution for ML practitioners, ensuring online-offline consistency.
- Oct 15, 07:40 AM UTC · 20 min · Presentation
› Serving Real-Time Features at Etsy
Learn more about Etsy’s Rivulet, a real-time feature store, enhancing ML performance by ensuring feature freshness.
- Oct 15, 08:10 AM UTC · 20 min · Presentation
› Shepherd: High-Scale, Low-Latency Machine Learning with Flink at Stripe
Stripe explores Shepherd's architecture: Flink, tiled data storage, and an automated control plane for faster feature development.
- Oct 15, 08:30 AM UTC · 20 min · Presentation
› From Dearth to Discovery: Anomaly Detection using Generative AI
Supriya discusses leveraging generative AI models for anomaly detection, crucial for early fraud detection and risk management.
- Oct 15, 08:30 AM UTC · 20 min · Presentation
› Real-time Feature Serving for Online Inference
Learn how Chalk built its just-in-time online feature store to enable realistic ML use-cases and serve requests in under 5 milliseconds.
- Oct 15, 08:50 AM UTC · 20 min · Presentation
› The Snowflake Schema Data Model comes to Feature Stores
Learn how Hopsworks now supports the Snowflake Schema Data Model, enabling more features via foreign keys in online tables.
- Oct 15, 09:10 AM UTC · 20 min · Presentation
› Feature Store as a Service: How Intuit's Feature Store Service Boosts Developer Productivity on One Intuit Platform
Discover Intuit's strategy for a millisecond-ready Feature Store, enabling developers to build faster, data-driven apps.
- Oct 15, 09:40 AM UTC · 15 min · Presentation
› Immutable KV Store on Cassandra
Learn how Uber's Michelangelo team uses Cassandra for online prediction, its limitations, and how an immutable store offers a solution.
- Oct 15, 09:55 AM UTC · 15 min · Presentation
› TurboML’s platform to leverage fresh data for ML
Learn how TurboML's platform overcomes the challenges posed by real-time data that enable fresher features, faster models and more.
- Oct 15, 10:10 AM UTC · 15 min · Presentation
› Enabling Low Latency Fraud Detection with Real-Time Feature Engineering
Learn how to build real-time fraud detection pipelines with Quix Streams, a Python library, for faster, simpler feature computation.
- Oct 15, 10:25 AM UTC · 15 min · Presentation
› Real-Time Fine-Tuning of Embedding Models for Improved Retrieval with Hopsworks and Bytewax
Learn to optimize continuous embedding models with Hopsworks and Bytewax for real-time improvements in content recommendation and more.
- Oct 15, 10:50 AM UTC · 15 min · Presentation
› Large-Scale Embedding Feature Generation at Uber
Explore how Uber uses embeddings for ML systems, covering their lifecycle, from creation to deployment, and their impact on performance.
- Oct 15, 11:05 AM UTC · 15 min · Presentation
› How Roche’s Data Platform accelerates Feature Engineering through Generative AI
Discover how Roche’s data platform uses GenAI to transform feature engineering, enhancing healthcare analytics.
- Oct 15, 11:20 AM UTC · 15 min · Presentation
› Fennel's Primitives for maintaining Data & Feature Quality
Learn seven key primitives from Fennel that ensure data and features are reliable and trustworthy for high-quality ML models.
- Oct 15, 11:50 AM UTC · 10 min · Wrap up
› Wrap-up
Jim Dowling wraps up the event.