Edge computing can improve data privacy and security by keeping the processing and storage more localized, reducing the risk of data breaches, and allowing for compliance with privacy regulations.
Edge computing offers processing power closer to its user, either on edge devices or local servers, versus relying solely on completely centralized cloud infrastructure to significantly reduce latency and bandwidth requirements.Cloud services can be combined with Edge Computing, which provides multiple benefits: Cloud platforms are innately well-designed for collaboration across geographies and organizations, which can help accelerate development and deployment.
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Cloud computing for AI is scalable and cost-effective, increases accessibility and allows companies to start with smaller, trial generative AI projects.Īccording to Grand View Research, the global edge computing market grew from $7.43 billion in 2021 to $11.24 billion in 2022, a 51% increase, and is expected to expand at a compound annual growth rate of 37.9% from 2023 to 2030. Cloud computing allows a company to dynamically scale up or down rather than incurring the cost of building out infrastructure scaled for the max load needed while training the algorithm. However, the often enormous data sets used for training an AI algorithm and the need for intense computational power can lead to latency issues when dealing with cloud services, thus the need for hybrid cloud/edge architecture.Ĭloud computing is well suited for running the algorithm during its training period when data and computational demands are typically the highest. According to KPMG, the cloud is expected to surpass on-premises infrastructure by 2024. Since it emerged in the early 2000s, modern cloud computing has become the leading solution for managing large-scale computational tasks and data storage, both of which are vital for generative AI.
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