The design updates AWS’s 2023 visual-inspection pipeline pattern. It replaces the deprecated SageMaker Edge Manager with SageMaker Neo for edge optimization. A camera-parameterized SageMaker Pipeline runs 13 steps, training an anomaly-detection autoencoder and a ResNet18 defect classifier for each camera. Both branches export to ONNX, evaluate held-out data, pass quality gates, compile through Neo, and register approved artifacts in SageMaker Model Registry.
AWS describes Step Functions orchestration that queues retraining work and limits concurrent execution within account quotas. The sample uses AWS CDK for deployment and includes SQS, Lambda, DynamoDB, S3, SNS, and a GitHub repository. AWS says one full run on synthetic data costs about $0.07 in compute.
The post does not establish costs, accuracy, throughput, or operational reliability for a real fleet. It also does not show that a per-camera model is required in every computer-vision deployment.