Zipline: A Declarative feature engineering framework

In this talk we will introduce zipline, a feature engineering framework developed at airbnb to help data scientists take features to production – safely and quickly. Zipline is used to a.) Generate point-in-time correct feature backfills for model training with high efficiency, b.) Generate online feature serving pipelines that can serve realtime feature aggregates with high availability, while also guaranteeing online-offline consistency. c.) Incorporate Change Data Streams into features for sub-second data freshness
Who is this presentation for?
For technically inclined audiences - intending to implement/deploy feature engineering solutions in their organizations.
Prerequisite knowledge:
Basic familiarity with machine learning workflows - in industry.
What you'll learn?
Understand strategies for temporal feature data aggregation to deal with rapidly changing data in typical web-scale companies.

Nikhil is a Software Engineer on the Machine Learning infrastructure team at Airbnb. He is currently working on Bighead, an end-to-end machine learning platform. Prior to Airbnb, he worked on schedulers, distributed real-time data processing engines and compilers at Facebook. Nikhil got his Bachelors degree in Computer Science from Indian Institute of Technology, Bombay. While not working, he likes to boulder or play capoeira.