FeatureHub

Online/offline consistent feature warehouse

Product category: Feature Store

Applicable platforms: Spark/Flink, Redis, Docker, K8s

Technical affiliation: Feature Store, Streaming Compute

Programming language affiliation: Python, SQL, Scala

Tags: #FeatureStore #ML Product

Type: Feature Store

FeatureHub uses DSL to define feature logic, writing once to generate offline batch processing and online KV computation and service simultaneously, eliminating training/online inconsistency. It records lineage, versions, statistical distributions, and usage frequency, supporting hot/cold feature layering and TTL strategies. For time series tasks, it has built-in sliding window/season/anomaly repair operators; for multimodal tasks, it provides embedding norms; for decision tasks, feature service can return multiple columns by key in milliseconds. FeatureHub treats features as "shared assets", teams no longer repeat wheel-building, experiments are reproducible, go-live is verifiable.

Delivery method: Instant digital download after purchase
License: Single-user commercial license
Usage limit: One-time use
Support: Technical documentation provided in the delivery file; no human technical support included