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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
238
|
第
7
章
计算和网络隔离同时也提升了安全性并降低了攻击者获取数据访问的风险。作为一
个安全最佳实践,所有
SageMaker
组件都应与不能直接连接互联网的虚拟私有云
(
Virtual Private Cloud
,
VPC
)一同使用。这需要我们仔细配置
IAM
角色,
VPC
端
点(
VPC Endpoints
),子网和安全组(
security groups
)的策略,使其对
Amazon
S3
,
SageMaker
,
Redshift
,
Athena
,
CloudWatch
以及任何在数据科学工作流中使用
的其他
AWS
服务具有最小权限的访问。在第
12
章,我们会更深入了解如何使用计
算隔离、网络隔离、
VPC
端点和
IAM
策略来保证数据科学环境的安全。
7.2
使用
SageMaker JumpStart
部署预先训练的
BERT
模型
SageMaker JumpStart
提供了来自
AWS
,
TensorFlow Hub
和
PyTorch Hub
的预构建
解决方案,以覆盖许多应用场景,如欺诈检测、预测性维护、需求预测、自然语言
处理(
NLP
)、物体检测和图像分类,如图
7-3
所示。
文本模型
为多种自然语言处理使用场景部署并调优预训练转换器。
视觉模型
为图片分类与物体检测一键部署并调优预训练模型。
社区模型
•
文本
社区模型
•
文本
社区模型
•
文本
社区模型
•
视觉 社区模型
•
视觉
社区模型
•
视觉
图 7-3:通过 SageMaker JumpStart 部署预训练的模型
SageMaker JumpStart
在我们需要迅速地在数据集上测试解决方案或模型,并生成一 ...
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Publisher Resources

ISBN: 9787519869663