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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
240
|
第
7
章
输出如下所示:
Review text: 'i simply love this product'
Predicted star_rating: 5
Review text: 'worst product ever'
Predicted star_rating: 1
7.3
开发一个
SageMaker
模型
正如
Amazon.com
通过
Amazom.com
市场为顾客提供许多选项,
Amazon SageMaker
为构建、训练、调优和部署模型也提供了许多选项。我们会在第
8
章模型调优和第
9
章部署模型时对此进行深入探讨。取决于所需的定制化程度,一共有三种主要的
选项,如图
7-5
所示。
内建算法 自带容器
自带脚本
代码更少 定制化程度更高
图 7-5:SageMaker 有三种选项进行模型构建、训练、优化和部署
7.3.1
内置算法
SageMaker
为许多不同领域提供可以开箱即用的内置算法,如
NLP
,计算机视觉,
异常检测和推荐。只需要将这些高度优化的算法指向我们的数据,我们将得到一个
完全训练的、便于部署的机器学习模型以集成到应用中。下表展示的算法被设计用
于很大的数据集且被成千上万的用户所使用,适合不想管理许多基础设施,而想要
重用这些身经百战的算法的我们。此外,它们也提供了一些便利,诸如大规模分布
式训练以降低训练时间,以及对混合精度浮点数(
mixed-precision floating-point
)
的支持以改善模型预测时延。
训练第一个模型
|
241
分类 计算机视觉 文本处理
• ...
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Publisher Resources

ISBN: 9787519869663