Paper Title

Generative AI vs. Machine Learning in Cloud Environments: An Analytical Comparison

Authors

ER. PRONOY CHOPRA , ER. OM GOEL , ROF.(DR.) ARPIT JAIN

Keywords

• Generative AI • Machine Learning • Cloud Environments • Natural Language Processing • Predictive Analytics • Data Privacy • Model Interpretability • Ethical Implications • Regulatory Considerations • Future Trends

Abstract

Generative AI and traditional machine learning (ML) have rapidly advanced, driven by their integration into cloud environments, which provide scalable infrastructure and services. This paper offers an analytical comparison of generative AI and machine learning within cloud ecosystems, emphasizing their unique characteristics, advantages, and challenges. Generative AI, characterized by its ability to create new content and mimic human-like creativity, has seen significant advancements with models such as GPT and GANs, transforming fields such as natural language processing, art, and design. In contrast, traditional ML models, which focus on predictive analytics and pattern recognition, are integral to various applications, including recommendation systems, fraud detection, and predictive maintenance. The cloud environment facilitates these technologies by providing essential computational resources, storage, and scalable frameworks, allowing rapid deployment and iterative improvements. However, challenges persist, including data privacy, model interpretability, and resource consumption. This paper explores the architectural differences between generative AI and traditional ML in cloud environments, highlighting their respective use cases and performance metrics. Through case studies, the paper examines how businesses leverage these technologies to drive innovation and efficiency. Furthermore, it discusses the ethical implications and regulatory considerations of deploying AI models in the cloud, addressing issues such as bias, transparency, and accountability. The paper concludes by exploring future trends and opportunities, proposing strategies for integrating generative AI and machine learning to maximize benefits and minimize risks. By providing a comprehensive comparison, this paper aims to inform researchers, practitioners, and policymakers about the potential and limitations of these transformative technologies in cloud environments.

How To Cite

"Generative AI vs. Machine Learning in Cloud Environments: An Analytical Comparison", JNRID - JOURNAL OF NOVEL RESEARCH AND INNOVATIVE DEVELOPMENT (www.JNRID.org), ISSN:2984-8687, Vol.1, Issue 3, page no.a1-a17, March-2023, Available :https://tijer.org/JNRID/papers/JNRID2303001.pdf

Issue

Volume 1 Issue 3, March-2023

Pages : a1-a17

Other Publication Details

Paper Reg. ID: JNRID_700576

Published Paper Id: JNRID2303001

Downloads: 000106

Research Area: Science and Technology

Country: -, -, India

Published Paper PDF: https://tijer.org/JNRID/papers/JNRID2303001

Published Paper URL: https://tijer.org/JNRID/viewpaperforall?paper=JNRID2303001

About Publisher

ISSN: 2984-8687 | IMPACT FACTOR: 9.57 Calculated By Google Scholar | ESTD YEAR: 2023

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: JNRID (IJ Publication) Janvi Wave

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