Scalable Architectures for Machine Learning in Multi-Cloud Environments

Abstract

This paper focuses on the characteristics of scalable architectures for machine learning in multi-cloud environments. The study is based on a comparative analysis of multi-cloud solutions that enable the integration of computing resources across various cloud providers, thereby optimizing costs, enhancing system flexibility, and reducing dependence on a single vendor. Particular attention is given to auto-scaling mechanisms that allow for efficient workload distribution under changing operational conditions, as well as to innovative approaches to data management within distributed infrastructures. The findings contribute to the theoretical and practical development of multi-cloud applications in the field of machine learning and may serve as a foundation for future research and the advancement of cloud computing technologies. The insights presented will be of interest to researchers and professionals in distributed computing and machine learning who are focused on designing and optimizing scalable architectures in multi-cloud contexts. The material may also prove valuable to IT industry specialists and academics seeking to integrate advanced algorithms with innovative architectural solutions to achieve maximum flexibility, fault tolerance, and performance in computing systems.