---
format: "aidr-story-markdown/v1"
id: "addcc356018895ea80768162a6cd19159fe30986a7dd58b737e40ad0718f716f"
canonical_url: "https://aidr.today/addcc356?lang=en"
title: "Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-10-26T17:56:58.000Z"
category: "Frameworks"
topics: ["amazon","inference","framework"]
source_urls: ["https://aws.amazon.com/blogs/machine-learning/fleet-wide-computer-vision-lifecycle-at-the-edge-with-sagemaker-sdk-v3/"]
summary: "A new Amazon SageMaker SDK v3 reference architecture trains, quality-gates, compiles, and versions per-camera computer vision models for entire fleets, using SageMaker Neo and AWS Step Functions to manage fleet-scale retraining within account quotas."
---

# Fleet\-wide computer vision lifecycle at the edge with SageMaker SDK v3

> [Open the canonical story](<https://aidr.today/addcc356?lang=en>)

**Published:** 2026-10-26T17:56:58.000Z
**Category:** Frameworks
**Topics:** amazon, inference, framework

## Summary

A new Amazon SageMaker SDK v3 reference architecture trains, quality\-gates, compiles, and versions per\-camera computer vision models for entire fleets, using SageMaker Neo and AWS Step Functions to manage fleet\-scale retraining within account quotas\.

## Sources

- [Story source](<https://aws.amazon.com/blogs/machine-learning/fleet-wide-computer-vision-lifecycle-at-the-edge-with-sagemaker-sdk-v3/>)