Integrating AI-Based Image Processing with Cloud-Native Computational Infrastructures for Scalable Analysis

Computer vision is one of the most popular and valuable tracks in AI, as far as it offers various ways of feature extraction and object detection, recognition, and enhancement. However, scalability becomes a major issue as image data increases. One such strategy that can harness a reliable solution is inherent in cloud native computational architectures, which make use of containers, microservices architecture, and serverless computing. The present paper aims to examine how to enhance the scalability and effectiveness of image processing with the help of AI and cloud environments. We consider the benefits of using AI for image analysis in the cloud, describe different models for implementing it and compare cloud providers. Moreover, it has been found by implementing these algorithms, a higher performance with less cost is achievable when dealing with huge images. This paper presents a detailed discussion of the potentially problematic issues in implementing AI models in cloud systems, including latency, security, and resources