Day 2
Oct 13, 2021
2:00 pm

Palette at Scale

The team at Uber talk about the advances of the Palette Feature Store that enables Feature Management at scale.

About this session

The Michelangelo ML Platform at Uber first introduced the concept of a Feature Store as a key component of the Machine Learning lifecycle. Since then, the Palette Feature Store has supported feature engineering needs across all major use cases at Uber. As the adoption rises, new challenges emerge. 

In this talk, we present the continued advances of the Palette Feature Store that enables Feature Management at scale. will deep dive into the scalability challenges emerging from Uber’s recommendation systems, the infrastructure we built to track the data quality of feature pipelines and models, and the mechanisms for enabling automatic selection of features from the feature store.

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