Architecting Low‑Latency Stateful Streaming Pipelines for High‑Performance Distributed Machine Learning
Introduction The rise of real‑time analytics, online personalization, and continuous model improvement has pushed the limits of traditional batch‑oriented machine‑learning (ML) pipelines. Modern applications—ranging from fraud detection to recommendation engines—must ingest massive streams of events, maintain per‑entity state, and feed that state into sophisticated ML models within milliseconds. Achieving such low latency while preserving stateful correctness and fault‑tolerance is non‑trivial. It requires a careful blend of streaming architecture, state management techniques, networking optimizations, and tight integration with distributed ML frameworks. ...