Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3
Amazon Web Services Kiowa Jackson
Amazon SageMaker SDK v3 is used to build a fleet-wide computer vision MLOps pipeline that trains anomaly and defect models per camera, evaluates them, and compiles approved artifacts for edge deployment. A full pipeline run on synthetic data costs about $0.07 in compute. Approved per-camera model versions are quality-gated and rate-limited for fleet retraining orchestration, while underperforming branches fail and do not get registered or shipped.
Why it matters
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.