feature-stores

What problem does it solve?

Feature stores centralize and govern the data used by machine learning models, enabling consistent, low-latency access to both historical and real-time features while reducing data leakage and drift.

Core Features & Use Cases

• Feature registry & versioning: Maintain a centralized catalog of features with lineage and version history.
• Feast-based implementation: Build feature views and offline/online stores to support training and real-time inference.
• Data validation & quality: Integrate data quality checks (e.g., Great Expectations) to ensure reliable features.
• Online/Offline serving: Provide unified feature access for training pipelines and live inference.
• Use Case: Realize a real-time recommendation system with fresh user features and batch backfills.

Quick Start

Quick Start: Install dependencies, configure Feast, create entities and feature views, run materialization, and validate data with the provided validation scripts.