05-02, 10:30–10:55 (Asia/Jerusalem), PyData Track 2
It’s good for feature reuse in machine learning, thereby increasing data science accuracy, velocity, and visibility.
A feature store is a single interface to create, discover, and access features for model training and inference. A wholistic feature store solution containing both storage and transformation layers would ideally include:
- Ingestion - both from streams and batch jobs
- Serving - low latency single features for inference and high throughput bulk features for training
- Transformation / Aggregation logic
- Discovery - features and how to retrieve them
This session will attempt to demonstrate why a feature store is useful, review current solutions, and provide a number of tips on getting started.
English
Target audience –Developers, Data Scientists, R&D
Orr is a ML Engineering Team Lead at Lemonade, currently developing a unified ML Platform. His team’s work aims to increase development velocity, improve accuracy, and promote visibility into machine learning at Lemonade.
Previously, Orr worked at Twiggle on semantic search, at Varonis, and at Intel. He holds a B.Sc. in Computer Science and Psychology from Tel Aviv University.
Orr also enjoys trail running and sometimes races competitively.