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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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mkurman/zorai --skill feast命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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正在显示 SKILL.md
| 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"] |
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.
uv pip install feast
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,
)
feast apply # register in registry
feast serve # online serving at localhost:6566