Big Data Meets Machine Learning: Tools, Trends, and Challenges
DOI:
https://doi.org/10.15680/IJCTECE.2019.0201002Keywords:
Big Data, Machine Learning, Distributed Computing, AutoML, Federated Learning, Scalability, Data Privacy, Model Interpretability, Environmental Impact, Data AnalyticsAbstract
The convergence of Big Data and Machine Learning (ML) has revolutionized data analytics, enabling organizations to extract actionable insights from vast and complex datasets. This paper explores the tools, emerging trends, and challenges at the intersection of Big Data and ML, providing a comprehensive overview of the current landscape. We examine the evolution of ML techniques tailored for Big Data environments, highlighting advancements in distributed computing, automated machine learning (AutoML), and federated learning. Additionally, we address the challenges associated with scalability, data privacy, model interpretability, and environmental impact. Through a systematic review of literature and case studies, we identify key strategies for overcoming these challenges and propose future directions for research and development. This paper serves as a valuable resource for researchers, practitioners, and policymakers seeking to navigate the complexities of integrating ML with Big Data analytics.
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