Vol. 2 No. 1 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Serverless AI: Building Scalable AI Applications without Infrastructure Overhead

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  • Naresh Dulam,
  • Babulal Shaik,
  • Karthik Allam
Naresh Dulam
Vice President Sr Lead Software Engineer, JP Morgan Chase, USA
Babulal Shaik
Cloud Solutions Architect, Amazon Web Services, USA
Karthik Allam
Big Data Infrastructure Engineer, JP Morgan & Chase, USA
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Published 04-05-2021

Keywords

  • Serverless AI,
  • scalable AI applications
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Naresh Dulam, Babulal Shaik, and Karthik Allam, “Serverless AI: Building Scalable AI Applications without Infrastructure Overhead ”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, pp. 519–542, May 2021, Accessed: Dec. 23, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/228

Abstract

Artificial intelligence (AI) has revolutionized industries, offering transformative capabilities in areas like healthcare, finance, retail, and beyond. Yet, building and scaling AI applications often come with the heavy burden of managing infrastructure, provisioning resources, and addressing scalability challenges. Serverless computing emerges as a game-changer, eliminating the need to manage servers while providing on-demand scalability and cost efficiency. This paradigm allows developers to focus solely on application logic and innovation, leaving infrastructure concerns behind. By combining serverless computing with AI, organizations can deploy intelligent, scalable applications faster and more economically. Serverless architectures operate on a pay-as-you-go model, ensuring that businesses only pay for the exact resources consumed during AI tasks like training, inference, or data processing. This approach significantly reduces operational costs while enabling effortless scaling for fluctuating workloads. Beyond cost benefits, serverless platforms simplify development, offering seamless integrations with machine learning tools, pre-trained models, and real-time data pipelines. Practical use cases span a wide range of industries—automating customer service with AI-powered chatbots, enabling dynamic personalization in e-commerce, streamlining fraud detection in finance, and driving innovation in predictive analytics. The serverless model democratizes access to cutting-edge AI technologies, making them accessible even to smaller organizations without extensive infrastructure budgets. Moreover, it allows larger enterprises to streamline operations, innovate faster, and enhance customer experiences without being constrained by infrastructure complexities. By leveraging serverless AI, developers and organizations can focus on solving real-world problems and delivering value, unburdened by the technicalities of server management. This convergence of serverless computing and AI not only simplifies the development lifecycle but also ensures that applications are resilient, scalable, and cost-effective. Ultimately, serverless AI empowers businesses to reimagine what’s possible, unlocking the full potential of intelligent applications while staying agile in an increasingly competitive and data-driven world.

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