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DISTRIBUTED MACHINE LEARNING SYSTEMS: ARCHITECTURES FOR SCALABLE AND EFFICIENT COMPUTATION (KEY IJW**********291)

• Arnab Kar, Ashish Kumar, Archit Joshi, Om Goel, Dr. Lalit Kumar, Prof. (dr.) Arpit Jain

Abstract

In recent years, the rapid growth of data has necessitated the development of advanced computational techniques to manage and analyze this information effectively. Traditional monolithic machine learning systems face significant limitations in terms of scalability, efficiency, and flexibility when dealing with large datasets and complex models. Distributed machine learning systems offer a promising solution to these challenges by leveraging multiple interconnected nodes to process data and execute learning tasks concurrently. This paper explores various architectures for distributed machine learning systems, focusing on their potential for scalable and efficient computation.