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Real-Time Feature Store

6 production-ready cookbooks for building a real-time ML feature store with Valkey. From basic HSET/HGET to streaming pipelines and production monitoring.

┌──────────────┐ ┌───────────────┐ ┌──────────────┐ │ ML Training │ │ Feature │ │ Valkey │ │ Pipeline │────▶│ Computation │────▶│ Online │ │ (offline) │ │ (streaming) │ │ Store │ └──────────────┘ └───────────────┘ └──────┬───────┘ │ ┌───────────────┐ │ HMGET │ Inference │◀───────────┘ (~0.1ms) │ Service │ └───────────────┘
01

Getting Started

Connect to Valkey, define entities & feature views, write and read features in under 5 minutes.

Beginner~5 minPython
02

Online Feature Serving

Sub-millisecond feature lookups with HGETALL, HMGET, batch pipelines, and feature vectors.

Intermediate~15 minPython
03

Real-Time Aggregations

Sliding window counts, rolling averages, and HyperLogLog cardinality - computed in Valkey.

Intermediate~20 minPython
04

Streaming Feature Updates

Real-time feature pipelines with Valkey Streams. Publish, consume, and materialize features instantly.

Advanced~20 minPython
05

ML Model Integration

Serve feature vectors to scikit-learn, PyTorch, and LLM chains. Fraud detection & recommendation examples.

Advanced~25 minPython
06

Production Patterns

Feature freshness monitoring, TTL strategies, versioning, health checks, and observability.

Advanced~30 minPython

🎮 Try It Live

Interactive dashboard - create entities, write features, fetch feature vectors, and see latency metrics.

Open Interactive Demo
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