mkurman Overview
Feast is an open-source feature store for production ML, providing offline (batch training data via SQL queries) and online (low-latency serving via Redis, Firestore, or DynamoDB) feature retrieval with point-in-time correctness. Features are versioned, validated, and governed through a registry.
Installation
uv pip install feast
Feature Definition
from feast import Entity, FeatureView, FileSource, ValueType
from datetime import timedelta
driver = Entity(name="driver_id", value_type=ValueType.INT64, description="Driver identifier")
source = FileSource(path="data/driver_stats.parquet", timestamp_field="event_timestamp")
feature_view = FeatureView(
name="driver_hourly_stats",
entities=[driver],
ttl=timedelta(hours=2),
source=source,
)
Serve
feast apply # register in registry
feast serve # online serving at localhost:6566