Optimizing AI Model Inference on Serverless Cloud Platforms: A Scalable Approach

Abstract : The increasing prevalence of Artificial Intelligence (AI) and Machine Learning (ML) models across various industries has highlighted the critical need for efficient and scalable deployment strategies. Traditional deployment methods often struggle with adapting to fluctuating demands and maintaining cost-effectiveness. Serverless computing has emerged as a promising solution to address these challenges. This paper investigates the deployment of AI models within serverless architectures on Amazon Web Services (AWS), specifically focusing on AWS Lambda and Knative. The study analyzes the limitations of conventional deployment approaches and proposes innovative strategies leveraging the capabilities of serverless technologies. Furthermore, it presents a rigorous evaluation of the performance characteristics of these serverless deployment strategies, discusses crucial security and privacy considerations, incorporates illustrative real-world case studies, and outlines potential future research directions.