Comparison of Distributed and Parallel Machine Learning: Efficiency and Effectiveness in Large-Scale Data Processing

Abstract

Abstract. As a matter of fact, with the exponential growth of data, machine learning (ML) techniques have increasingly relied on distributed and parallel computing to handle large-scale problems. With this in mind, this paper provides a comparative analysis of distributed and parallel machine learning methodologies, focusing on their efficiency and effectiveness in processing large datasets. To be specific, this study will discuss as well as contrast key models and frameworks within both paradigms, assessing their performance based on computational cost, scalability, and accuracy. Through empirical evidence and case studies, this research will highlight the strengths and limitations of each approach. According to the analysis, the findings indicate that while distributed machine learning excels in scalability as well as fault tolerance, parallel machine learning offers superior computational speed for smaller-scale tasks. Overall, the insights from this study are crucial for researchers and practitioners seeking to optimize ML workflows for large-scale data environments.