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Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3

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Fleet-wide computer vision needs a model per camera, not one model for the whole fleet. This post presents a working Amazon SageMaker SDK v3 reference architecture that trains, quality-gates, compiles per-camera models with SageMaker Neo, and versions thousands of them, with an AWS Step Functions layer pacing fleet-scale retraining within account quotas.

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Fleet-wide computer vision model lifecycle management is a problem that shows up the moment a quality inspection proof of concept succeeds. One camera and one model work fine. Then the plant asks for the same result on 40 lines, each with several cameras, and every camera sees the world differently. Each has a different angle, different glare off the same stainless surface, and different ambient light depending on which bay door is open. In production deployments we have worked on, a single…

O overfeed.news indexa e aponta. Publicamos um trecho curto — o artigo completo fica em AWS Machine Learning Blog.

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Fleet-wide computer vision lifecycle at the edge with SageMaker…