<![CDATA[The growing ubiquity of Artificial Intelligence (AI) and Machine Learning (ML) models in diverse sectors has underscored the essential requirement for effective and scalable deployment methodologies. Conventional deployment methods frequently encounter difficulties in adjusting to variable demands and sustaining cost efficiency. Serverless computing has arisen as a viable way to tackle these difficulties. This article examines the implementation of AI models in serverless architectures on Amazon Web Services (AWS), with particular emphasis on AWS Lambda and Knative. The research examines the constraints of traditional deployment methods and suggests novel solutions utilizing the advantages of serverless technology. Additionally, it offers a thorough assessment of the performance attributes of various serverless deployment methodologies, addresses essential security and privacy issues, includes pertinent real-world case studies, and delineates prospective avenues for future research]]>
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