feast
$
npx mdskill add mkurman/zorai/feastServe validated features for production ML pipelines.
- Enables offline batch training and low-latency online serving.
- Integrates with SQL queries, Redis, Firestore, and DynamoDB.
- Uses a registry to version, validate, and govern features.
- Delivers point-in-time correct data via REST or SQL.
SKILL.md
.github/skills/feastView on GitHub ↗
---
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
```bash
uv pip install feast
```
## Feature Definition
```python
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
```bash
feast apply # register in registry
feast serve # online serving at localhost:6566
```
## References
- [Feast docs](https://docs.feast.dev/)
- [Feast GitHub](https://github.com/feast-dev/feast)