Preparing Data for Machine Learning with Cloud Infrastructure: Methods and Challenges

ER. SHANMUKHA EETI; A RENUKA; ER. PANDI KIRUPA GOPALAKRISHNA PANDIAN

Preparing Data for Machine Learning with Cloud Infrastructure: Methods and Challenges

Authors: ER. SHANMUKHA EETI, A RENUKA, ER. PANDI KIRUPA GOPALAKRISHNA PANDIAN

Unique Paper ID: 167453
Volume: 9
Issue: 8
PageNo: 923-929

Keywords: Machine Learning Cloud Infrastructure Data Preparation Scalability Data Privacy Data Transformation Big Data Cost Management Data Quality

Abstract:
In the age of big data, machine learning (ML) is increasingly critical for extracting insights from large datasets. The rise of cloud infrastructure has transformed data processing, offering scalable resources and cost-effective solutions for machine learning tasks. This paper explores the methods and challenges associated with preparing data for machine learning in a cloud environment. Key processes include data collection, cleaning, transformation, and integration, all essential for optimizing ML models. Challenges such as data privacy, security, and latency are also addressed. The paper further reviews the advantages of using cloud platforms for ML, including scalability, flexibility, and ease of collaboration. Despite these benefits, there remain significant challenges, particularly concerning data transfer, cost management, and ensuring the quality of data preparation. This study provides a comprehensive overview of current methodologies and identifies research gaps that suggest avenues for future exploration. By leveraging cloud infrastructure effectively, organizations can enhance their ML capabilities, resulting in more accurate predictions and better decision-making.

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Cite This Article

ISSN: 2349-6002
Volume: 9
Issue: 8
PageNo: 923-929

Preparing Data for Machine Learning with Cloud Infrastructure: Methods and Challenges

Available:https://ijirt.org/Article?manuscript=167453

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