Skill v1.0.0

version: "1.0.0" name: feast description: "Feast — open-source feature store. Online and offline serving, point-in-time joins, feature validation, and streaming ingestion. Standardizes feature management across training and production." tags: [feast, feature-store, mlops, feature-engineering, online-serving, infrastructure, zorai]

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

References

• Feast docs
• Feast GitHub