Scholarlink

Cloud computing has revolutionized the information technology landscape, significantly impacting the development and deployment of Artificial Intelligence and Machine Learning technologies. This research explores the synergy between cloud computing and AI ML, examining how cloud infrastructure supports these technologies by providing computational power, data handling capabilities, and scalability. By evaluating different cloud service models such as IaaS, PaaS, and SaaS and deployment strategies such as public, private, hybrid, serverless, and edge, the study highlights the adaptability and effectiveness of cloud solutions in addressing AI ML needs. The paper critically reviews major cloud service providers, including Amazon Web Services, Google Cloud Platform, and Microsoft Azure, each offering unique strengths in AI ML services, scalability, and security. Despite the substantial benefits, challenges such as security vulnerabilities, privacy risks, and management complexities are discussed, with strategies for mitigation. The proposed conceptual framework provides a structured approach to understanding how cloud computing facilitates AI ML adoption and optimization, emphasizing continuous improvement.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Leveraging Cloud Computing for Enhanced Artificial Intelligence and Machine Learning: A Comprehensive Analysis of Service Models, Deployment Strategies, and Provider Offerings

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Chin Hao Zac,
  • Muhammad Ehsan Rana,
  • Manoj Jayabalan,
  • Vazeerudeen Abdul Hameed

摘要

Cloud computing has revolutionized the information technology landscape, significantly impacting the development and deployment of Artificial Intelligence and Machine Learning technologies. This research explores the synergy between cloud computing and AI ML, examining how cloud infrastructure supports these technologies by providing computational power, data handling capabilities, and scalability. By evaluating different cloud service models such as IaaS, PaaS, and SaaS and deployment strategies such as public, private, hybrid, serverless, and edge, the study highlights the adaptability and effectiveness of cloud solutions in addressing AI ML needs. The paper critically reviews major cloud service providers, including Amazon Web Services, Google Cloud Platform, and Microsoft Azure, each offering unique strengths in AI ML services, scalability, and security. Despite the substantial benefits, challenges such as security vulnerabilities, privacy risks, and management complexities are discussed, with strategies for mitigation. The proposed conceptual framework provides a structured approach to understanding how cloud computing facilitates AI ML adoption and optimization, emphasizing continuous improvement.