From Legacy to Building Our Own Feature Store: Active Steps

on YouTube ↗40m

" summary

Describes migrating from legacy feature handling to building an in-house Feature Store for an organization with many ML models. Covers integrating the new Feature Store into existing data pipelines incrementally while continuing to deliver business requirements, the operational challenges encountered, and what to expect when standardizing feature pipelines at scale.

AI-written summary · about

" chapters (11)

1. 0:00Introduction
2. 3:21Legacy system problems
3. 7:30Implementation approach
4. 10:43Data storage and pipeline
5. 14:42Data delivery and quality
6. 18:54Data catalog and DAG
7. 21:48Model service workflow
8. 23:33Future platform plans
9. 26:29Business impact and TTM
10. 28:17When to build your own
11. 29:21Q&A

" related talks

• 30mHow Feature Stores Enable Operational MLMike Del Balso · TWIMLcon: AI Platforms 2021
• 08mFeature Store Usage Patterns - From a Single Data Scientist to an EnterpriseSimba Khadder · Feature Store Summit 2022
• 35mPanel 03- How to make the Feature Store become a hidden part of the productionalization of ML modelsAchint Thomas · Feature Store Summit 2022
• 32mA feature Store for Machine LearningJim Dowling · PyData Fest Amsterdam 2020
• 20mNavigating the Skies of Data: A Flight Through Feature StoresDave Ruijter · SQLBits 2024
• 10mFast Sub-ML Usecase Development using Feature StoresAchint Thomas · Feature Store Summit 2022
• 25mBuilding A Feature Store For Hyper GrowthBrian Seo · Feature Store Summit 2022
• 15mFeature Stores: Powering Machine Learning Infrastructure with Apache FlinkSimba Khadder · Flink Forward Global Virtual 2020