Fleet-wide computer vision model lifecycle management is a problem that shows up the moment a quality inspection proof of concept succeeds. One camera and one model work fine. Then the plant asks for the same result on 40 lines, each with several cameras, and every camera sees the world differently. Each has a different angle, different glare off the same stainless surface, and different ambient light depending on which bay door is open. In production deployments we have worked on, a single model trained on pooled footage underperforms enough individual cameras that each camera ends up with its own fine-tuned model. Now the real problem is no longer training one model well. It’s training, evaluating, versioning, and shipping thousands of them without a human in the loop for each one.
This post updates the pattern introduced in the 2023 series Build an end-to-end MLOps pipeline for visual quality inspection at the edge for the current Amazon SageMaker Python SDK v3. It replaces the since-deprecated Amazon SageMaker Edge Manager with Amazon SageMaker Neo for edge optimization. I walk through a dual-branch Amazon SageMaker Pipeline that trains an anomaly detector and a defect classifier per camera, with quality gates that block underperforming models. An in-pipeline SageMaker Neo compilation step ensures every registered model ships with a device-optimized artifact, and an AWS Step Functions orchestration layer paces fleet training. You deploy everything with the AWS Cloud Development Kit (AWS CDK), and a full pipeline run on synthetic data costs about $0.07 in compute.
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