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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基于
AWS
的数据科学概述
｜
31
会启动指定数量的资源，基于
S3
数据运行大规模批量预测。在任务完成之后，
SageMaker
会将数据写入
S3
并销毁计算资源。
1.7.3
通过
AWS Lambda
实现无服务器模型部署
提供模型预测服务的另一个选项是
AWS Lambda
函数提供的无服务器模型服务。在
使用
SageMaker
训练模型之后，我们使用
AWS Lambda
函数从
S3
拉取模型并提供
预测服务。
AWS Lambda
有内存和时长的限制，所以在完成部署前，请切记对这个
选项规模化测试。
1.8 AWS
上的流分析和机器学习
直到现在，我们假定我们在一个像基于
S3
的数据湖的中心化静态位置保存了所有
的可用数据。现实中，数据持续从许多来自世界各地的数据源同时以流的方式接入
进来。在许多场景下，我们要在流数据到达数据湖之前就进行实时分析和机器学习。
企业需要尽可能缩短得到（商业）洞见的时间来获得竞争优势并能够迅速响应持续
变化的客户和市场趋势。
流技术为我们提供了实时收集、处理并分析数据的工具。
AWS
提供了广泛的流技术
方案，包括
Amazon Kinesis
和
Amazon Managed Streaming for Apache Kafka
（
Amazon
MSK
）。我们将在第
11
章深入介绍流分析和机器学习。
1.8.1 Amazon Kinesis Streaming
Amazon Kinesis
是一个帮助我们实时收集、处理和分析流数据的服务。借助
Kinesis
Data Firehose
，我们可以持续地准备并加载实时数据到不同的目的地，包括 ...
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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.