Scalable AI: Leveraging Cloud and Edge Computing for Real-Time Analytics

Scalable AI, Cloud Computing, Edge Computing, Real-time Analytics, Artificial Intelligence, Internet of Things (IoT), Latency Reduction, Data Privacy, Distributed Computing, AI Frameworks

Authors

  • Vinay Chowdary Manduva Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, India., India
November 28, 2024

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With the rapid advancements in artificial intelligence (AI), the demand for scalable solutions to process vast amounts of data in real time has become more critical than ever. Traditional computing infrastructures often struggle with the computational and storage requirements necessary to support AI applications, especially for real-time analytics. This paper explores how combining cloud computing with edge computing can address these challenges by offering scalable, low-latency, and highly efficient AI systems for real-time decision-making. It examines the architectural frameworks, key technologies, and potential applications of AI in cloud and edge computing environments. Additionally, the paper investigates various methodologies for enhancing AI performance, data privacy, and communication efficiency. Finally, it discusses real-world case studies where this hybrid approach has been successfully implemented.