---
name: mkurman/feast
source: https://app.decimal.ai/s/mkurman-feast@1/SKILL.md
source_sha256: 40eb37620d51
---

## 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)