Applying Object-Oriented Principles to Machine Learning Frameworks for Improved Scalability
Keywords:
Object-Oriented Programming, Machine Learning, Scalability, Frameworks, Modularity, ReusabilityAbstract
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.