Articles

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

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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.

Cloud-Native Data Science for Edge Computing and IoT Applications

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The use of edge computing and the Internet of Things are now considered essential subcategories of contemporary data systems. There is a new wave of data science application deployment approaches and management modularity, also referred to as cloud-native which caters for the required distribution to edge devices. The factors under consideration in this paper are emerging cloud-native technologies that include containerization, microservices, and the serverless model in data science workflows for edge computing and the Internet of Things. The above insights reveal the effectiveness of using this approach in supporting organizations for data science to create highly generalized, safe, and efficient data systems that meet the demands of edge working settings. Innovative city solutions, health care, and industrial Internet of Things are the important areas examined, and additional prospects and concerns are introduced.