Applying Object-Oriented Principles to Machine Learning Frameworks for Improved Scalability

Authors

  • Dr. Yash Raj Lodhi Assistent Professor, Fr Conceicao Rodrigues College of Engineering Band Stand Bandra West) Mumbai 400 050, India Author
  • Dr. S.V. S. Shukla Assistant Professor, Fr Conceicao Rodrigues College of Engineering Band Stand Bandra West) Mumbai 400 050, India Author

Keywords:

Object-Oriented Programming, Machine Learning, Scalability, Frameworks, Modularity, Reusability

Abstract

The scalability of machine learning (ML) frameworks is a key factor in the successful deployment and training of large-scale models. With the ever-increasing complexity of ML tasks and datasets, there is a critical need for frameworks that can effectively manage computational resources while remaining flexible and maintainable. Object-Oriented Programming (OOP) principles offer a promising approach for enhancing scalability in ML frameworks. This paper investigates the integration of OOP principles into ML frameworks, emphasizing modularity, reusability, and maintainability. By analyzing existing frameworks, this research highlights the potential benefits of OOP in addressing scalability challenges and proposes guidelines for designing scalable ML systems using OOP principles.

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Published

30-11-2024

How to Cite

[1]
Dr. Yash Raj Lodhi and Dr. S.V. S. Shukla, “Applying Object-Oriented Principles to Machine Learning Frameworks for Improved Scalability”, International Journal of Sciences and Innovation Engineering, vol. 1, no. 3, pp. 10–17, Nov. 2024, Accessed: Dec. 23, 2024. [Online]. Available: https://ijsci.com/index.php/home/article/view/21

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