企业 AI 数据准备实际需要多长时间？
AI 数据准备时间线的诚实基准——按数据类型、量级和管道复杂度——以及拖慢企业 AI 项目的最大时间消耗。
诚实的答案是：比你预算的长。几乎普遍如此。
60-80% 的统计数据——ML 项目用于数据准备的时间比例——被广泛引用，持续被真正经历过企业 AI 项目的团队确认。
粗略基准表
| 数据集大小 | 格式 | 标注类型 | 估计总准备时间 |
|---|---|---|---|
| 1,000 文档 | 原生 PDF | 文档分类 | 2-4 周 |
| 1,000 文档 | 扫描 PDF | 文档分类 | 4-8 周 |
| 10,000 文档 | 原生 PDF | NER（5 实体类型） | 3-6 个月 |
| 10,000 文档 | 扫描 PDF | NER（5 实体类型） | 5-10 个月 |
| 50,000 文档 | 混合格式 | 指令 fine-tuning 对 | 6-18 个月 |
团队一贯低估的地方
- OCR 在遗留扫描文档上的质量
- 积累档案中的近似重复率
- 标签一致性和校准时间
- 目标框架的格式要求
手动流程比自动化管道可靠地产生 2-4 倍更长的时间线。Ertas Data Suite 的自动化阶段将总准备时间减少 40-60%。
Your data is the bottleneck — not your models.
Ertas Data Suite turns unstructured enterprise files into AI-ready datasets — on-premise, air-gapped, with full audit trail. One platform replaces 3–7 tools.
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