Make with H2O.ai: Getting Started with H2O AI Feature Store

Name: Make with H2O.ai: Getting Started with H2O AI Feature Store
Uploaded: 2022-10-11
Duration: 57 min
Description: AT&T's Prince Paul Raj and H2O's head of products on why AT&T partnered with H2O to build H2O AI Feature Store, with a demo of how the feature store works.

Predictive AI
H2O Feature Store
H2O Driverless AI

Overview

In this session, H2O.ai's head of products and AT&T's Prince Paul Raj, AVP of Data Science in AT&T's Chief Data Office, walk through why AT&T partnered with H2O.ai to build the H2O AI Feature Store. They explain the problem of duplicated, inconsistent, and hard-to-audit features across data science teams, then demo how the feature store centralizes, versions, and shares features across offline and online use cases. The session closes with a live demo covering feature search, SQL and Power BI access for citizen data scientists, Driverless AI integration, and real-time scoring.

Key takeaways

Why data science teams end up rebuilding the same features repeatedly, and how a feature store solves reuse and discovery
How AT&T uses the feature store to track feature lineage and versioning for model audits and reproducibility
The difference between the offline and online store, and how time travel lets you recreate point-in-time snapshots for backtesting
How data engineers, data scientists, citizen data scientists, ModelOps, and compliance teams all work from the same feature store
How to register a feature set, ingest data, and connect it to H2O Driverless AI to publish derived features back to the store
How to expose features for real-time scoring, including a live demo of an online lookup and a feature-store-powered chat agent

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