Server Less AI: Democratizing Machine Learning with Cloud Functions

Abstract

Large-scale distributive Artificial Intelligence (AI) systems that can cooperatively complete difficult learning tasks must replace traditional centralised AI systems in order to support emerging cross-device applications for AI. Because of its significant impact on cost savings, latency reduction, scalability improvement, and the elimination of server-side management, among other benefits, Server Less computing has become more and more prominent over the past 10 years as an intriguing new sector. Nevertheless, comprehensive surveys that would aid academics and developers in comprehending the importance of server-less computing in various scenarios are still lacking. The idea of server-less machine learning is examined in this study, along with how it can revolutionise the creation and application of AI algorithms. Engineers can concentrate on creating and refining machine learning models instead of worrying about maintaining the fundamental framework by utilising server-less platforms. The study explores the advantages of server-less machine learning, such as lower the time to market, scalability, and cost. It also covers how server-less machine learning may be integrated with widely used platforms and services to provide seamless model deployment and education. The difficulties of data management, performance optimisation, and security assurance in server-less machine learning settings are also covered in the study. The report attempts to shed light on an opportunity of Server Less technology in democratising AI development and speeding cloud innovation through this thorough analysis.