Managing fleet-wide edge computer vision models with SageMaker SDK v3

What happened

Kiowa Jackson presents a SageMaker SDK v3 reference architecture for training, evaluating, versioning, and preparing per-camera computer vision models for edge devices. Its dual-branch pipeline evaluates exported ONNX artifacts, applies quality gates, compiles passing models with SageMaker Neo, and registers them with camera metadata for subsequent retraining. Step Functions and a DynamoDB semaphore limit concurrent pipeline executions, while the accompanying GitHub sample supports deployment through AWS CDK.

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The story so far

AWS published a SageMaker SDK v3 reference architecture and accompanying GitHub sample for training, evaluating, versioning, and preparing per-camera computer vision models for edge devices. The walkthrough updates AWS’s 2023 pattern for visual quality inspection at the edge, with the sample supporting deployment through AWS CDK. The dual-branch pipeline evaluates exported ONNX artifacts, applies quality gates, compiles passing models with SageMaker Neo, and registers them with camera metadata for subsequent retraining. Step Functions and a DynamoDB semaphore limit concurrent pipeline executions. By default, the sample generates synthetic production-line images; the repository also includes a BCCD dataset downloader for running it with real images. AWS says a full run completes all 13 steps, including both Neo compilations, in about 15 minutes, with roughly $0.07 in billed ml.m5.xlarge time at July 2026 on-demand rates in US East (Ohio).