System Construction of Artificial Intelligence Dependent Cloud Service Mode based on Deep Learning Core Algorithms

In recent years, with the rapid development of computer technology, artificial intelligence, an important branch of the discipline, has been undergoing constant changes. The essence of artificial intelligence is the use of computer technology to simulate the human way of thinking and a series of intelligent behaviors, such as co-ordination, thinking, reasoning and so on. In intelligent robots, AI (artificial intelligence) language processing systems, image recognition systems and various types of expert systems, complex computer algorithms are used to simulate certain logical reasoning of the human brain, so as to achieve the intelligence of equipment. Against the background of global economic integration and accelerated informationization, artificial intelligence technology is developing rapidly. This paper presents a distributed model cloud platform based on Kubernetes architecture for data transfer, distributed concurrent multitasking and functional business requirements through virtualization and model componentization techniques. The platform adopts a three-tier structure of IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service), and combines with ArcGIS and modern Web technologies to realize efficient visualization display. We evaluate the performance of AWS, Google Cloud, Azure, and IBM Cloud in terms of deep learning model training and inference performance, and the experimental results show that Google Cloud and Azure are the best performers in terms of training time and accuracy as well as inference speed, while AWS and IBM Cloud excel in terms of scalability and security. Ultimately, this paper provides an important reference for organizations when choosing a cloud service provider to support deep learning applications.