September 2022
Intermediate to advanced
549 pages
11h 27m
Chinese
Content preview from 基于AWS的数据科学实践
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444
｜
第
10
章
随着模型变得越准确，发送至工作者的审核任务变得越少。此概念也被称为“主动
学习”且在
SageMaker Ground Truth
中得以实现。
10.7.2
借助
SasgeMaker Ground Truth
实现主动学习反馈
回路
主动学习以人类标记工作流作为起始，并在收集到足够的样本后转变为自标记。主
动学习反馈回路用于持续重训练模型并提升未来标记预测的置信度。主动学习通过
处理高置信度预测帮助扩展数据标记流程，解放劳动力以专注在需要人类智力的低
置信度预测上。
Amazon SageMaker Ground Truth
是一个服务于自动数据标记的增强
AI
工作流实现。
借助足够的数据，
SageMaker Ground Truth
将人类审核工作流与主动学习结合。随
着人类劳动力标记越来越多的数据，
SageMaker Ground Truth
主动训练模型以加入
劳动力，并在新数据到达时运行自动化标记。若模型对标记不够有信心，数据将发
送至人类劳动力进行审核。图
10-16
展示了
SageMaker Ground Truth
工作流以及从
手动到自动标记的转变。
缺乏置信的
标记数据
置信度足够的
标记数据
更新自定义
模型
自定义模型进行预测
（标记输入数据）
人类对输入
进行标记
Ground Truth主动学习
并生成自定义模型
保存于Amazon S3
当中的未标记
训练数据
保存于Amazon S3
的已标记
训练数据
图 10-16：SageMaker Ground Truth 使用主动学习来增强人类的数据标记
SageMaker ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.