Optimizing Image Classification Models for Cloud Infrastructure with Elastic Scaling

Advances in image classification have led to significant improvements in various applications, yet challenges remain in optimizing these models for cloud infrastructures. Fluctuating workloads and the need for efficient resource allocation complicate the deployment of high-performing models. We propose a novel framework that enhances image classification performance specifically within cloud environments through Elastic Scaling. This approach leverages a modular architecture, facilitating real-time adjustments to computing resources based on demand and complexity of the models being utilized. Our methodology incorporates diverse data preprocessing techniques and evaluates multiple model architectures to strike a balance between accuracy and operational efficiency. Extensive experiments conducted across various datasets demonstrate the effectiveness of our solution, showcasing considerable enhancements in classification performance and substantial reductions in operational costs tied to cloud infrastructure. The findings highlight improvements in response times and resource utilization, creating an adaptive framework that effectively addresses differing image processing requirements. This work offers a comprehensive solution aimed at optimizing cloud-based image classification tasks while maximizing performance and efficiency.