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基于AWS的数据科学实践
book

基于AWS的数据科学实践

by Chris Fregly, Antje Barth
September 2022
Intermediate to advanced
549 pages
11h 27m
Chinese
China Electric Power Press Ltd.
Content preview from 基于AWS的数据科学实践
218
|
第
6
章
6.3
通过
SageMaker
特征存储(
Feature Store
)
共享特征
特征工程需要直觉、耐心和试错。由于越来越多的团队在商业场景中利用
AI
和机
器学习,对一个易于发现且重用的中心化特征仓库的需求日渐增长。这种仓库被称
为“特征存储”(
Feature Store
)。
特征存储是存储机器学习特征的数据湖。正如我们在上文演示的用
SageMaker
处理
任务计算
BERT
特征那样,由于特征有时需要繁重的计算,如果可能,我们想要在
整个组织范围内存储并重用这些特征。
为
SageMaker
机器学习工作流而生的特征存储,和为商业智能报表和大屏服务的
Amazon Redshift
数据仓库,二者所需的转换可能不同。例如,我们在特征存储中保
存
BERT
嵌入,而在数据仓库中保存清洗且丰富过的数据,如图
6-14
所示。
应用
转换过的
数据
原始
数据集
特征存储
(AI和ML特征)
数据仓库
(SQL表)
数据湖
交易
点击流
应用数据
图 6-14:特征存储、数据湖和数据仓库的关系
无需自行构建特征存储,我们可以利用
Amazon SageMaker
托管的特征存储。
SageMaker
特征存储可以保存离线和在线特征。离线(
offline
)特征保存在为高吞
吐量、批量获取的工作流(如模型训练)而优化的仓库中。在线(
online
)特征保
存在为低延时实时请求(如模型推理)而优化的仓库中。
由于我们已经花费了一大笔时间生成
BERT
特征,我们想要将这些特征共享给我们
组织中的其他团队。或许这些团队能够从特征中发现我们不曾探索的新的和更优的 ...
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Publisher Resources

ISBN: 9787519869663