Assessing the Role of Scalable Machine Learning Architectures in Enhancing Predictive Accuracy and Decision Latency in Real-Time Big Data Analytics Environments

Abstract

In the era of high-velocity, high-volume data streams, real-time analytics plays a crucial role in enabling timely and accurate decision-making. This paper examines the influence of scalable machine learning (ML) architectures on enhancing predictive accuracy and reducing decision latency in big data environments. Through a comparative analysis of distributed ML systems such as Apache Spark MLlib, TensorFlow Extended (TFX), and FlinkML, the research explores how architectural decisions affect performance metrics across various real-time analytical applications. The study combines insights from previous works and experimental benchmarking using stream-based datasets. Results suggest that architecture scalability significantly enhances prediction efficacy while maintaining low latency under increased data loads, making it indispensable for next-generation intelligent analytics platforms.