Citation :

Kushal Walia, 2024. "Scalable AI Models through Cloud Infrastructure" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 2: 1-7.

Abstract :

In the rapidly evolving field of artificial intelligence (AI), the scalability of AI models has emerged as a critical factor determining their efficacy and applicability across various domains. This paper explores the integral role of cloud infrastructure in addressing the scalability challenges faced by contemporary AI models. Through an in-depth analysis, it elucidates how cloud infrastructure not only offers a solution to the computational demands of large-scale AI models but also facilitates efficient data management and deployment strategies for AI applications. By examining case studies and leveraging insights from current research, the paper highlights the synergistic relationship between cloud computing and AI scalability, underscoring the flexibility, cost-effectiveness, and enhanced performance capabilities afforded by cloud platforms. Furthermore, it delves into the technical, ethical, and cost-related challenges inherent in scaling AI models on the cloud, proposing strategies to mitigate these issues. Looking ahead, the paper discusses emerging trends in cloud infrastructure that promise to further augment the scalability of AI models, such as advancements in edge computing and the potential of quantum computing. The paper concludes by emphasizing the ongoing importance of research and innovation at the intersection of AI scalability and cloud infrastructure, suggesting that this dynamic interplay will significantly shape the future trajectory of AI development. Through its comprehensive analysis, the paper contributes valuable insights into the pivotal role of cloud infrastructure in enabling scalable AI models, offering a foundational perspective for future research and application in the field.

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Keywords :

Artificial Intelligence, Cloud Computing, Data Privacy, Scalability, Security.