Machine Learning Algorithms Scaling on Large-Scale Data Infrastructure

Abstract

Scalability is a critical aspect of deploying machine learning (ML) algorithms on large-scale data infrastructure. As datasets grow in size and complexity, organizations face challenges in efficiently processing and analyzing data to derive meaningful insights. This paper explores the strategies and techniques employed to scale ML algorithms effectively on extensive data infrastructure. From optimizing computational resources to implementing parallel processing frameworks, various approaches are examined to ensure the seamless integration of ML models with large-scale data systems.