Image recognition has evolved rapidly from rule-based systems to deep learning models, driven by the exponential growth in visual data and computing power. Traditional on-premise solutions struggle to meet the demands of large-scale, realtime image processing due to limitations in scalability, cost, and operational efficiency. This research addresses the challenge of building a scalable and cost-effective AI image recognition system by leveraging cloud infrastructure. The proposed method integrates deep learning-based image classification with Amazon Web Services (AWS), utilizing EC2 for computation, SQS for asynchronous task handling, and S3 for persistent storage within a modular, auto-scalable architecture. The system demonstrates high throughput, elastic resource management, and reliable classification accuracy across dynamic workloads. Results confirm enhanced performance, cost efficiency, and fault tolerance, making it a viable solution for diverse industries such as healthcare, security, and smart surveillance.
Mrs. Elakia K (Sun,) studied this question.