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Amazon SageMaker overview – Amazon Web Services models, avoiding overprovisioned compute and ensuring AI spend is tied directly to business outcomes rather than just tracking adoption. Infrastructure that compounds in efficiency over time is what separates a sustainable AI program from an expensive science project.”
That economic pragmatism applies doubly at the edge, where blindly streaming raw sensor feeds to the cloud creates unsustainable operating costs.
“ For us, being production-grade means being cost-effective as well as computationally effective,” Cormac says.“‘ When it has to be right’ applies to the economics of a solution too. If an AI model delivers the right answer but costs twice the available budget to run, it hasn’ t solved the customer’ s problem. One important principle is to avoid sending huge volumes of raw data to the cloud when useful processing can happen closer to where that data is generated. By pushing more classification, filtering and decision-making towards the sensor or edge, you can send smaller, more useful datasets downstream.”
On the horizon Looking ahead, the sheer pace of AI innovation demands total infrastructure flexibility.
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