Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Modern machine learning systems face significant challenges in optimizing data flow for feature computation within real-time inference pipelines. This comprehensive article explores architectural patterns and implementation strategies for balancing inference latency, model accuracy, and data freshness when features must be derived from heterogeneous sources, including transactional databases, third-party APIs, telemetry streams, and data lakes. It presents a detailed examination of the decoupling feature lifecycle from model execution through techniques such as feature pre-computation, caching with appropriate TTL settings, asynchronous updates, and just-in-time computation. Event-driven architectures leveraging serverless computing resources are evaluated alongside implementation strategies including tiered caching, adaptive TTL policies, and parallel feature computation. A case study of an e-commerce recommendation system demonstrates how these approaches dramatically reduced latency and improved conversion rates. The article concludes with emerging research directions in adaptive feature computation, model-guided feature freshness, and federated feature computation, highlighting their potential to further optimize the balance between computational efficiency and model accuracy while addressing privacy concerns in sensitive domains.