Intelligent Framework in a Serverless Computing for Serving using Artificial Intelligence and Machine Learning

Abstract

Serverless computing has grown in popularity as a paradigm for deploying applications in the cloud due to its ability to scale, cost-effectiveness, and simplified infrastructure management. Serverless architectures can benefit AI and Machine Learning (ML) models, which are becoming increasingly complex and resource-intensive. This study investigates the integration of AI/ML frameworks and models into serverless computing environments. It explains the steps involved, including model training, deployment, packaging, function implementation, and inference. Serverless platforms’ auto-scaling capabilities allow for seamless handling of varying workloads, while built-in monitoring and logging features ensure effective management. Continuous integration and deployment pipelines simplify the deployment process. Using serverless computing for AI/ML models offers developers scalability, flexibility, and cost savings, allowing them to focus on model development rather than infrastructure issues. The proposed model leverages performance forecasting and serverless computing model deployment using virtual machines, specifically utilizing the Knative platform. Experimental validation demonstrates that the model effectively predicts performance based on specific parameters with minimal data collection. The results indicate significant improvements in scalability and cost efficiency while maintaining optimal performance. This performance model can guide application owners in selecting the best configurations for varying workloads and assist serverless providers in setting adaptive defaults for target value configurations.