AWS Machine Learning Blog· Kiowa Jackson·· 5 h ago
Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3
Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3
AI summary
Fleet-wide computer vision needs a model per camera, not one model for the whole fleet. This post presents a working Amazon SageMaker SDK v3 reference architecture that trains, quality-gates, compiles per-camera models with SageMaker Neo, and versions thousands of them, with an AWS Step Functions layer pacing fleet-scale retraining within account quotas.
Selection record
Threshold 60Official, first-handFirst 34Second 34
Not admittedSum of both 68 < twice the threshold 120
- Source tier
- Official, first-hand; this tier's threshold is 60
- Pre-filter
- passed：SageMaker边缘视觉模型全生命周期教程
A model scores each item twice, independently, against one written standard, out of 100. An item is admitted only when the two scores add up to twice the threshold. The threshold is set per source tier.
Source: AWS Machine Learning Blog · aws.amazon.com