迁移指南：从碎片化数据工具到统一管道
你在用Docling解析、Label Studio标注、Cleanlab做质量检查、自定义脚本导出。以下是如何整合到单一平台而不丢失现有工作。
典型的企业AI数据准备技术栈：Docling解析、Label Studio标注、Cleanlab质量检查、DVC版本控制，加上一堆Python脚本做导出。5-7个工具，由自定义胶水代码连接，由写它的那个人维护。
为什么团队要迁移
- 审计追踪缺口 — 工具间交接无记录
- 维护负担 — 每次工具更新可能级联故障，15-25%时间用于维护
- 格式转换开销 — 每次交接需要自定义转换器
- 无单一负责人 — 数据质量下降时无法定位根因
迁移顺序
- 阶段1：导出（第1-2周） — 最低风险起步
- 阶段2：标注（第3-6周） — 最高价值
- 阶段3：质量检查（第5-7周）
- 阶段4：摄入（第7-9周） — 最后迁移入口
- 阶段5：验证和切换（第9-12周）
常见陷阱
- 试图一次迁移所有
- 不保留标注历史
- 低估格式差异
- 忘记处理中的工作
- 跳过并行运行
迁移到统一平台后团队报告：维护时间减少40-60%，完整审计追踪，调试从几天缩短到几分钟。
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