Scalable Machine Learning Solutions for Heterogeneous Data in Distributed Data Platform

Abstract

As the volume and variety of data continue to expand, the need for scalable machine learning solutions becomes increasingly vital, especially in distributed data platforms handling heterogeneous data sources. This research explores methods and techniques for developing scalable machine learning solutions tailored to the challenges posed by heterogeneous data in distributed environments. By addressing issues such as data variety, scalability, and distributed processing, this study aims to provide insights into building robust machine learning models capable of handling diverse data types efficiently. Through experimentation and analysis, the research seeks to uncover effective strategies for implementing scalable machine learning solutions in distributed data platforms, thereby facilitating improved data processing and decision-making capabilities across various domains.