Advanced Strategies For AI Model Deployment In   Cloud Environment

Author(s)	Janavi G prabhakar, G R Amrutha, Ambaldhage Anusha
Country	India
Abstract	Deploying AI models in cloud environments comes with several challenges, including security risks, cost inefficiencies, latency concerns, and performance issues. These obstacles can hinder the widespread adoption and scalability of AI-powered applications. This paper investigates strategies such as Zero Trust Security models, federated learning, autoscaling, serverless AI, multi-cloud deployment, and 5G-enabled edge computing to optimize AI scalability and efficiency. The study also delves into model optimization techniques, including quantization, pruning, and knowledge distillation, aimed at improving inference speed while reducing computational costs. Experimental results show that combining cloud-edge hybrid models with autoscaling and model optimization leads to cost savings, better security, and enhanced real-time AI processing
Keywords	Real-time AI Processing, Cloud-Edge AI, Cost Optimization, Model Optimization, Zero Trust Security
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-05-11
DOI	https://doi.org/10.71097/IJSAT.v16.i2.4936
Short DOI	https://doi.org/g9kc6t

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology