This paper is published in Volume-11, Issue-1, 2025
Area
Computer Science
Author
Shubham Malhotra
Org/Univ
Rochester Institute of Technology, Rochester, NY, USA
Keywords
Distributed Machine Learning, Cloud Computing, Big Data, Optimization, Parallel Processing. Cloud Computing, Parallel Processing, Scalability, Fault Tolerance, Data Replication
Citations
IEEE
Shubham Malhotra. The Current State of Research into the Efficiency of Distributed Machine Learning Algorithms for Cloud-Based Big Data Analysis, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Shubham Malhotra (2025). The Current State of Research into the Efficiency of Distributed Machine Learning Algorithms for Cloud-Based Big Data Analysis. International Journal of Advance Research, Ideas and Innovations in Technology, 11(1) www.IJARIIT.com.
MLA
Shubham Malhotra. "The Current State of Research into the Efficiency of Distributed Machine Learning Algorithms for Cloud-Based Big Data Analysis." International Journal of Advance Research, Ideas and Innovations in Technology 11.1 (2025). www.IJARIIT.com.
Shubham Malhotra. The Current State of Research into the Efficiency of Distributed Machine Learning Algorithms for Cloud-Based Big Data Analysis, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Shubham Malhotra (2025). The Current State of Research into the Efficiency of Distributed Machine Learning Algorithms for Cloud-Based Big Data Analysis. International Journal of Advance Research, Ideas and Innovations in Technology, 11(1) www.IJARIIT.com.
MLA
Shubham Malhotra. "The Current State of Research into the Efficiency of Distributed Machine Learning Algorithms for Cloud-Based Big Data Analysis." International Journal of Advance Research, Ideas and Innovations in Technology 11.1 (2025). www.IJARIIT.com.
Abstract
Today, data has become a driving force in nearly every business sector, and cloud computing, alongside artificial intelligence (AI), serves as a critical enabler that enhances business operations and performance. This research focuses on optimizing distributed machine learning (DML) algorithms within cloud environments to efficiently handle and process large datasets. The paper introduces a methodology for improving the performance of DML algorithms by utilizing the computational power and storage capacity of cloud platforms, coupled with parallel processing techniques. The experimental results demonstrate that the proposed approach reduces processing time by 40% and improves model accuracy by 15%, making it highly suitable for big data environments.