How can you optimize cloud and edge resource allocation?
Cloud and edge computing are two paradigms that enable the deployment of AI applications and services in different scenarios. Cloud computing offers centralized, scalable, and cost-effective resources, while edge computing provides distributed, low-latency, and privacy-preserving resources. However, optimizing the allocation of cloud and edge resources for AI tasks is not a trivial problem. It involves balancing various factors, such as performance, cost, security, reliability, and user preferences. In this article, you will learn some strategies and techniques to optimize cloud and edge resource allocation for AI.
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Raphaël MANSUYData Engineering | DataScience | AI & Innovation | Author | Follow me for deep dives on AI & data-engineering
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Shubham SabooLinkedIn Top Voice | AI Product Management at Tenstorrent | 3x Author of AI Books | Microsoft MVP | Community of 100k+…
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Anesu KafesuCTO | IT and Infrastructure Project Management | PMP® | ITIL 4® | CCNA | CISM® | OCIAA