Real-Time Feature Serving Expert
Design and operate low-latency feature delivery systems for production ML, including feature store architecture, online/offline consistency, streaming pipelines, and serving infrastructure optimization.
SupaScore
82.9Best for
- ▸Design feature store architecture for production ML with sub-10ms latency requirements
- ▸Eliminate training-serving skew in real-time recommendation systems
- ▸Build streaming feature pipelines with Kafka/Flink for near-real-time fraud detection
- ▸Optimize Redis/DynamoDB online stores for high-throughput feature serving
- ▸Implement point-in-time correct joins for financial risk modeling training datasets
What you'll get
- ●Detailed architecture diagram with feature classification (batch/streaming/real-time), technology stack recommendations, and latency budgets for each component
- ●Streaming pipeline implementation plan using Kafka Streams with windowing functions, state stores, and exactly-once semantics for feature computation
- ●Point-in-time join implementation strategy with SQL patterns, offline-to-online materialization schedules, and feature validation framework
Not designed for ↓
- ×Basic batch ETL pipelines without real-time serving requirements
- ×Model training or algorithm selection (focuses on feature delivery, not ML models)
- ×Static feature engineering for offline analysis
- ×Simple key-value caching without ML feature semantics
Clear feature serving requirements including latency SLAs, throughput targets, data sources, and consistency requirements between training and inference environments.
Detailed feature store architecture with technology selections, streaming pipeline designs, consistency patterns, and implementation roadmaps with latency optimization strategies.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 8 sources (4 official docs, 2 paper, 1 academic, 1 industry frameworks)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial release
Prerequisites
Use these skills first for best results.
Works well with
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Specialist skills that go deeper in areas this skill touches.
Common Workflows
ML Platform Feature Infrastructure
End-to-end ML feature platform: define feature requirements, design real-time serving architecture, integrate with ML deployment pipeline
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