2021-05-02, 10:30–10:55, 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.