Real-Time Data Streaming Architectures for Machine Learning-Driven Decision Systems

Abstract

Real-time decision-making applications require data pipelines capable of processing streaming data with minimal latency. This paper examines modern streaming architectures supporting machine learning inference and feedback loops. The research compares event-driven designs using message brokers and stream processors, highlighting trade-offs in scalability, fault tolerance, and operational complexity.