Agenda 2022

Kick-off

Jim Dowling will talk about the featurestore.org community and kickstart the event.

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Accelerating ML at Uber with the Palette Feature Store

Amit will show how the Michelangelo ML Platform uses the Palette Feature Store to address inefficiencies of the model lifecycle.

Hopsworks Feature Store after 4 years: Lessons learned and what's next

Hopsworks will share the lessons learned over the past four years of building their platform and what lies ahead.

Building A Feature Store For Hyper Growth

Brian will go over Doordash's feature store architecture, learnings from supporting CRDB and what it takes to build a feature store.

Feathr: An Enterprise-Grade, High Performance Feature Store

David and Xiaoyong will cover the background of Feathr, its core concepts and design, and their journey on scaling an enterprise FS.

APIs for Feature Stores

A discussion on the historical evolution and the future of APIs for feature stores.

Nexus Feature Store powering Disney Magic

Dustin will describe his team's journey towards building Nexus, an in-house feature store and how it accelerates feature engineering.

Introducing Zipline: An Open Source Feature Engineering Platform

Nikhil will introduce Zipline, a declarative feature engineering platform developed at Airbnb, which will be open-sourced in March.

Fighting against Marketplace Scams using Community-driven AI

Sinan will cover how Shiba uses a Feature Store to maintain ML models with up-to-date data on how bad actors target communities.

Feature Store Observability: What it is, and why it matters

Aman will discuss the state of ML production monitoring, its challenges, and how to actively improve models and features in production.

Data Quality & Feature Stores

A discussion on challenges of data quality for features and whether differentiate from general data quality requirements for analytics.

Hamilton: an open source, declarative, micro-framework for clean & robust feature transform code in Python

Stefan will present Hamilton an open source Python micro framework that solved his team's pain points by changing their working paradigm.

America First Credit Union optimizes their MLOps stack with the Hopsworks Feature Store

Richard will elaborate on how AFCU's adoption of the Hopsworks Feature Store has helped them significantly improve their workflows.

Fast Sub-ML use-case development using feature stores

Achnit will talk about Sub-ML, a class of applications simpler than traditional ML approaches and often used in decision support systems.

Feature Store Usage Patterns - From a Single Data Scientist to an Enterprise

Simba from Featureform will share the team's learnings from different companies on usage patterns of feature stores.

How to make the Feature Store become an invisible part of the productionalization of ML models

A discussion on the challenges of making the feature store disappear and become part of the workflow of data science and data engineering.

Powering Enterprise Feature Stores with a Universal Semantic Layer

Gaurav will focus on how enterprises can apply the power of the semantic layer to enrich feature stores and scale business-ready AI.

Empowering your feature stores with AI feature discovery

Ryohei from dotData will discuss how Feature Discovery and Feature Factory concepts can transform your feature development process.

Scaling Feature Engineering with Dagger

Ravi will discuss how Dagger can be used with feature stores to empower data scientists to make feature engineering self-service.

Designing Feature DSLs: Principles and Tradeoffs

Greg from Sumatra will discuss feature designs and describe their journey developing a DSL for streaming feature transformation.

ralf: Real-time, Accuracy Aware Feature Store Maintenance

Sarah, PhD will introduce the notion of feature store regret that helps evaluate feature quality of different maintenance policies.

OpenMLDB: An Open-Source Real-Time Feature Platform Computing Consistent Features for Training and Inference

Lu introduces OpenMLDB, an open-source ML database that provides a real-time feature platform for ML applications that reduces dev cost.

Closing

Jim Dowling, Co-Founder and CEO, Hopsworks.

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Agenda

Kick-off

Accelerating ML at Uber with the Palette Feature Store

Hopsworks Feature Store after 4 years: Lessons learned and what's next

Building A Feature Store For Hyper Growth

Feathr: An Enterprise-Grade, High Performance Feature Store

APIs for Feature Stores

Nexus Feature Store powering Disney Magic

Introducing Zipline: An Open Source Feature Engineering Platform

Fighting against Marketplace Scams using Community-driven AI

Feature Store Observability: What it is, and why it matters

Data Quality & Feature Stores

Hamilton: an open source, declarative, micro-framework for clean & robust feature transform code in Python

America First Credit Union optimizes their MLOps stack with the Hopsworks Feature Store

Fast Sub-ML use-case development using feature stores

Feature Store Usage Patterns - From a Single Data Scientist to an Enterprise

How to make the Feature Store become an invisible part of the productionalization of ML models

Powering Enterprise Feature Stores with a Universal Semantic Layer

Empowering your feature stores with AI feature discovery

Scaling Feature Engineering with Dagger

Designing Feature DSLs: Principles and Tradeoffs

ralf: Real-time, Accuracy Aware Feature Store Maintenance

OpenMLDB: An Open-Source Real-Time Feature Platform Computing Consistent Features for Training and Inference

Closing

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