Version 1
: Received: 7 September 2024 / Approved: 7 September 2024 / Online: 10 September 2024 (09:00:24 CEST)
How to cite:
Kottakota, S. S.; Gnaneswar, A. Secured Artificial Intelligence Based Face Anti-spoofing Detection Model via Serverless Architecture and SaaS Based Cloud Platform. Preprints2024, 2024090740. https://doi.org/10.20944/preprints202409.0740.v1
Kottakota, S. S.; Gnaneswar, A. Secured Artificial Intelligence Based Face Anti-spoofing Detection Model via Serverless Architecture and SaaS Based Cloud Platform. Preprints 2024, 2024090740. https://doi.org/10.20944/preprints202409.0740.v1
Kottakota, S. S.; Gnaneswar, A. Secured Artificial Intelligence Based Face Anti-spoofing Detection Model via Serverless Architecture and SaaS Based Cloud Platform. Preprints2024, 2024090740. https://doi.org/10.20944/preprints202409.0740.v1
APA Style
Kottakota, S. S., & Gnaneswar, A. (2024). Secured Artificial Intelligence Based Face Anti-spoofing Detection Model via Serverless Architecture and SaaS Based Cloud Platform. Preprints. https://doi.org/10.20944/preprints202409.0740.v1
Chicago/Turabian Style
Kottakota, S. S. and Anantha Gnaneswar. 2024 "Secured Artificial Intelligence Based Face Anti-spoofing Detection Model via Serverless Architecture and SaaS Based Cloud Platform" Preprints. https://doi.org/10.20944/preprints202409.0740.v1
Abstract
As the adoption of deep learning models continues to surge across various applications, the need for efficient deployment architectures becomes increasingly critical. This paper presents a novel approach to enhance the deployment of deep learning models by leveraging serverless architecture. Serverless computing has been popular for its auto-scaling, cost-effectiveness, and simplified management characteristics. However, the intense resource demands of deep learning models pose challenges in maintaining low response times and effective load balancing within serverless environments. The proposed architecture addresses these challenges by integrating principles from both deep learning model optimization and serverless computing. Through systematic experimentation and analysis, we demonstrate that by appropriately designing and tuning the deployment architecture, significant improvements in response time, performance, resource utilization, and load distribution can be achieved.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
