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

This article demonstrates a fleet-wide computer vision model lifecycle management system using SageMaker SDK v3 for managing thousands of per-camera models in production quality inspection deployments.

• Dual-branch SageMaker Pipeline trains anomaly detector and defect classifier per camera with quality gates blocking underperforming models
• SageMaker Neo compilation step ensures every registered model ships with device-optimized artifacts for edge deployment
• Model Registry uses camera metadata to enable per-camera warm-start training and incremental re-training across fleets
• Step Functions orchestration with DynamoDB semaphore paces fleet training within AWS account service quotas
• Quality gates evaluate exported ONNX artifacts using balanced accuracy plus minority-class recall for imbalanced defect data
• Complete sample deploys via AWS CDK with full pipeline run on synthetic data costing approximately $0.07 in compute

The architecture enables scalable, automated management of thousands of per-camera models with quality assurance and edge optimization built into the pipeline.