Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3
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This article presents a reference architecture for managing the computer vision model lifecycle across large fleets of edge devices using the Amazon SageMaker Python SDK v3. It addresses the challenge of deploying thousands of specialized models by automating training, evaluation, and deployment without human intervention for each camera. The solution utilizes a dual-branch pipeline that trains both an anomaly detector and a defect classifier per camera, ensuring quality through gates based on balanced accuracy and recall metrics. Models are compiled with Amazon SageMaker Neo for device optimization before being registered in the SageMaker Model Registry. An orchestration layer built with AWS Step Functions and DynamoDB manages concurrent training requests to stay within account service quotas. The entire system is deployed via AWS Cloud Development Kit (CDK) and can be validated end-to-end using synthetic data for approximately seven cents in compute costs. This pattern enables efficient, fleet-wide updates while maintaining high-quality standards for industrial visual inspection tasks.
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