Scalable Machine Learning Algorithms: Techniques, Challenges, and Future Directions

Abstract

Scalable machine learning algorithms are crucial for handling large-scale datasets and complex models in modern data-driven applications. This paper reviews the state-of-the-art techniques for scalability, explores the challenges involved, and discusses future directions for research and development in this field. Key areas of focus include distributed computing, efficient data processing, and advancements in algorithmic design.