AI Reports · Amazon · 2026-10-26 · Blog / commentary
Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3
Fleet-wide computer vision needs a separate model for each camera, since each camera sees the scene differently. This post presents a SageMaker Python SDK v3 reference architecture that trains and quality-gates per-camera models, compiles them with SageMaker Neo, and versions thousands of them. AWS Step Functions paces retraining within account quotas, and a full pipeline run on synthetic data costs about $0.07 in compute.