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

原文摘要 · 中英对照

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.

车队级计算机视觉需要为每个摄像头配备一个模型，而不是整个车队共用一个模型。本文介绍了一个可工作的Amazon SageMaker SDK v3参考架构，该架构使用SageMaker Neo训练、质量把关、编译每个摄像头的模型，并对数千个模型进行版本管理，同时通过AWS Step Functions层在账户配额内调节车队规模的重新训练。