METHODS FOR CREATING NETWORKS SUPPORTING ARTIFICIAL INTELLIGENCE USING CLOUD TECHNOLOGIES
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Keywords: Containerization with Kubernetes, Data Parallelism, Leveraging Specialized Hardware, Latency and Bandwidth Constraints, Networking Architecture.##article.abstract##
Abstract: The rapid advancement of Artificial Intelligence (AI) has transformed industries, enabling advanced data processing, predictive analytics, and automation. However, the computational demands of AI workloads, particularly for training large-scale models like deep neural networks, require robust and scalable network infrastructures. Cloud technologies have emerged as a cornerstone for building such networks, offering flexibility, scalability, and cost-efficiency. This article explores the methods for creating networks that support AI applications using cloud technologies, addresses associated challenges, and proposes solutions. It also incorporates mathematical formulations to quantify key aspects of network performance and resource allocation. The integration of cloud computing with AI enables organizations to leverage distributed resources, high-performance computing (HPC), and specialized hardware like GPUs and TPUs. However, challenges such as latency, data privacy, and resource optimization must be addressed to ensure efficient AI network performance. This article provides a detailed examination of these methods, supported by formulas and practical solutions.
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