Year: 2025
Type: article
Abstract: This comprehensive technical article explores the evolution and implementation of real-time AI systems through stream processing pipelines. It explores the transformation from traditional batch processing to dynamic, real-time intelligence, highlighting the architectural components, implementation patterns, and industry applications. The article discusses critical aspects of stream processing foundations, AI/ML integration layers, and real-time inference engines while addressing challenges in feature engineering, model deployment, and system monitoring. Through detailed analysis of applications across financial services, healthcare, and insurance sectors, the article demonstrates how organizations leverage real-time AI to achieve significant improvements in operational efficiency, decision-making capabilities, and customer service delivery. The article also explores emerging trends, including edge computing integration, federated learning, and quantum computing applications, providing insights into the future direction of real-time AI systems.
Author: Himanshu Adhwaryu
Institution: George Washington University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407870802