
云到边缘AI管道:数据准备如何在训练和部署之间适配
完整的云到边缘AI管道从原始数据到设备部署。数据准备是原始企业数据和云训练之间的步骤——也是大多数边缘AI项目失败的地方。
云到边缘AI管道有七个阶段。大多数企业团队关注其中三个——训练、量化和部署——然后纳闷为什么边缘模型表现不佳。
缺失的部分是数据准备。不是通用数据准备,而是专门为边缘部署约束设计的准备。为70B云模型产生强结果的数据集会为0.5B边缘模型产生弱结果。
完整管道
阶段1:原始数据收集(5%项目时间) 阶段2:数据准备(40-60%项目时间) 阶段3:云训练(10%项目时间) 阶段4:模型蒸馏(5%项目时间) 阶段5:量化和优化(5%项目时间) 阶段6:运行时导出(2%项目时间) 阶段7:设备部署和验证(15%项目时间)
搞错的代价
| 方法 | 数据准备时间 | 训练迭代 | 总上线时间 |
|---|---|---|---|
| 通用数据准备 → 部署到边缘 | 3周 | 5-7次迭代 | 14-20周 |
| 从一开始就考虑边缘的数据准备 | 4周 | 2-3次迭代 | 8-11周 |
Ertas Data Suite完全在本地处理阶段2,作为原生桌面应用。
Turn unstructured data into AI-ready datasets — without it leaving the building.
On-premise data preparation with full audit trail. No data egress. No fragmented toolchains. EU AI Act Article 30 compliance built in.
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