Feature Store Summit 2022
2022-10-11..2022-10-14
-- online --
| title | author | length |
|---|---|---|
| Kick-off | Jim Dowling | 15m |
Hopsworks claims to be the first open-source, enterprise, and serverless feature store and cites Uber Michelangelo as the origin of the idea. The feature store… | ||
| Accelerating ML at Uber with the Palette Feature Store | Amit Nene | 25m |
Uber describes Palette as a feature store inside the Michelangelo platform that automates pipeline generation, point‑in‑time snapshotting and online/offline… | ||
| Hopsworks Feature Store after 4 years: Lessons learned and what's next | Fabio Buso, Moritz Meister | 23m |
After four years building Hopsworks the team claims to have solved the core “data write” and “data access” problems by combining an offline/online store… | ||
| Building A Feature Store For Hyper Growth | Brian Seo | 25m |
Doordash builds an internal declarative framework fabricator that makes a feature one-line deploy and available to models within hours, which triggers… | ||
| Feathr: An Enterprise-Grade, High Performance Feature Store | David Stein, Xiaoyong Zhu | 23m |
Feathr is LinkedIn’s feature store running in production for 5+ years, open-sourced April 2022, supporting hundreds of models and thousands of features at… | ||
| Panel 01 - APIs for Feature Stores | Amit Nene | 30m |
Panel argues that DSLs succeed when they guarantee symmetric online/offline execution by restricting transformations so low‑latency serving is feasible, while… | ||
| Nexus Feature Store powering Disney Magic | Dustin Hamerla | 21m |
An internal system named Nexus provides a dual feature store: a row‑oriented low‑latency store and a columnar offline store used for batch training and… | ||
| Introducing Zipline: An Open Source Feature Engineering Platform | Nikhil Simha | 26m |
Chronon is an open source feature engineering platform (rebranded from Zipline) that claims point‑in‑time correctness, online/offline consistency, and… | ||
| Fighting against Marketplace Scams using Community-driven AI | Sinan Ozdemir | 22m |
Claims a production pipeline that ingests Discord community reports and message metadata, stores engineered features in a feature store, and scores messages in… | ||
| Feature Store Observability: What it is, and why it matters | Aman Khan | 21m |
Argues that feature stores need active observability not just alerts and claims a survey of ~1,000 companies shows incidents take about a week to detect and… | ||
| Panel 02 - Data Quality & Feature Stores | Aman Khan | 34m |
Panel lays out concrete checks and tools: three failure modes—training data quality, serving data quality, and online-offline consistency—with solutions like… | ||
| Hamilton: an open source framework for feature transform code in Python | Stefan Krawczyk | 21m |
Hamilton is an open source Python micro framework that models feature transforms as named functions with explicit inputs, a driver that scrapes modules to… | ||
| America First Credit Union optimizes their MLOps stack with Hopsworks | Richard Woolston | 18m |
America First converts Oracle SQL centric pipelines to PySpark and replaces Oracle RDBMS with Hopsworks and HopsFS, integrating Redgate, Airflow, Great… | ||
| Fast Sub-ML Usecase Development using Feature Stores | Achint Thomas | 10m |
Argues for Sub-ML: simpler, decision‑support applications that need correctness, transparency, and auditability but not full ML stacks. Uses a Scribble Data… | ||
| Feature Store Usage Patterns - From a Single Data Scientist to an Enterprise | Simba Khadder | 08m |
Claims that a feature store mainly solves the organizational life cycle not heavy lifting compute and demonstrates a virtual feature store that layers on top… | ||
| Panel 03- How to make the Feature Store become a hidden part of the productionalization of ML models | Achint Thomas | 35m |
Panelists argue feature stores must stop being a separate product and become an orchestrator plus metadata layer that integrates with Snowflake, BigQuery… | ||
| Powering Enterprise Feature Stores with a Universal Semantic Layer | Gaurav Rao | 22m |
Claim: a universal semantic layer (AtScale example) lets enterprises populate and enrich feature stores so models actually deliver value. Demonstrated: a… | ||
| Empowering your feature stores with AI feature discovery | Lulu Liu | 10m |
Tool from dotData automatically generates features on Databricks using Delta Lake and Spark, exploring about 100,000 feature hypotheses and returning a… | ||
| Scaling Feature Engineering with Dagger | Ravi Suhag | 18m |
Dagger is a cloud-native framework built on Apache Flink that runs feature engineering as SQL first pipelines with optional Python or Java UDFs and GitOps YAML… | ||
| Designing Feature DSLs: Principles and Tradeoffs | Greg Kuhlmann | 13m |
A bespoke DSL called scowl DSL expresses streaming and on‑demand feature transforms as composable components rather than opaque batch jobs. It implements a… | ||
| ralf: Real-time, Accuracy Aware Feature Store Maintenance | Sarah Wooders | 09m |
Feature-store maintenance splits into two concrete systems: ralf uses a scheduling policy that tracks per-key cumulative regret (error accumulated from stale… | ||
| OpenMLDB: An Open-Source Real-Time Feature Platform | Lu Mian | 12m |
OpenMLDB packages a Spark-improved batch SQL engine, a realtime SQL engine built from scratch, and a unified execution plan generator so the same SQL feature… | ||
| nothing matches | ||