A Scalable Framework for Machine Learning Models in Real-Time Big Data Analytics Environments

Abstract

The rapid growth of big data has created new challenges for processing and analyzing information in real-time. Machine learning models must now operate efficiently at scale while maintaining accuracy and speed. This study proposes a scalable framework for deploying machine learning models in real-time big data analytics environments. It examines the integration of distributed computing technologies, such as cloud platforms and parallel processing systems, to handle large volumes of data. The research focuses on optimizing model performance through techniques like data streaming, incremental learning, and model compression. Additionally, it evaluates the trade-offs between scalability, latency, and predictive accuracy. Real-world applications, including financial forecasting, smart city systems, and online recommendation engines, are used to demonstrate the effectiveness of the proposed framework. The findings suggest that scalable machine learning systems require a balance between computational efficiency and model complexity. This study provides practical guidelines for designing robust and adaptive analytics systems capable of meeting the demands of modern data-driven environments.