{"id":"6f86219c-6e2a-412e-ac32-ceea0976cb74","url":"https://api.tumn.com/news/6f86219c-6e2a-412e-ac32-ceea0976cb74","title":"Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3","summary":"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 A","sections":[],"faq":[],"sources":[{"url":"https://aws.amazon.com/blogs/machine-learning/fleet-wide-computer-vision-lifecycle-at-the-edge-with-sagemaker-sdk-v3/","title":"AWS Machine Learning Blog","publishedAt":"2026-10-26T17:56:58.000Z"}],"author":"TUMN 큐레이션","aiAssisted":false,"reviewStatus":"automated","reviewNote":"원문 제목과 공개된 발췌를 인용하고 원문으로 연결합니다. 내용을 재작성하거나 요약하지 않았습니다.","publishedAt":"2026-10-26T17:56:58.000Z","modifiedAt":"2026-10-26T20:26:22.000Z","checkedAt":"2026-10-26T20:26:22.772Z","language":"ko","kind":"link","sourceLabel":"AI 공식","profile":null,"via":null,"formats":{"json":"https://api.tumn.com/news/6f86219c-6e2a-412e-ac32-ceea0976cb74.json","markdown":"https://api.tumn.com/news/6f86219c-6e2a-412e-ac32-ceea0976cb74.md"}}