Dynamic Data Scaling Techniques for Streaming Machine Learning

Abstract

This research delves into innovative dynamic data scaling techniques designed for streaming machine learning environments. In the realm of real-time data streams, conventional static scaling methods may encounter challenges in adapting to evolving data distributions. To overcome this hurdle, our study explores dynamic scaling approaches capable of adjusting and optimizing scaling parameters dynamically as the characteristics of incoming data shift over time. The objective is to augment the performance and adaptability of machine learning models in streaming scenarios by ensuring that the scaling process remains responsive to changing patterns in the data. Through empirical evaluations and comparative analyses, the study aims to showcase the efficacy of the proposed dynamic data scaling techniques in enhancing predictive accuracy and sustaining model relevance in dynamic and fast-paced streaming environments. This research contributes to the advancement of scalable and adaptive machine learning methodologies, particularly in applications where timely and accurate insights from streaming data are crucial.