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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242
｜
第
7
章
代码更少 定制化程度更高
内建算法 自带脚本
自带容器
图 7-7：使用 BERT 和 TensorFlow 的 SageMaker 脚本模式既可以高度定制，又不需要大
量维护
7.3.3
自定义容器（
Bring Your Own Container
）
定制化程度最高的选项即“自定义容器”。这一选项使我们构建并部署我们自己的
Docker
容器到
SageMaker
。这一
Docker
容器可以包含任何库或框架。虽然我们对
训练脚本及其依赖拥有绝对控制权，
SageMaker
管理日志、监控、注入环境变量、
注入超参数、映射数据集输入和输出位置等底层基础设施。这一选项适合具有更底
层系统背景的机器学习从业者，或出于合规性和安全性的原因需要使用自己
Docker
容器的场景。将已有
Docker
镜像进行转换使其运行于
SageMaker
是十分简单直接的，
只需要遵循此
AWS
开源项目列出的步骤（
https://oreil.ly/7Rn86
）。
7.4
自然语言处理简史
在第
6
章，我们将原始的
Amazon
客户评价转换成
BERT
特征向量，用以最终构建
评价分类器模型来从
review_body
文本预测
star_rating
。在构建自然语言模型之前，
我们需要提供一些关于
NLP
的背景知识。
在
1935
年，一位举世闻名的英国语言学家
J. R. Firth
提出，“词汇的完整含义通常
与上下文有关，任何脱离上下文进行词意分析的研究都不能够当真。”近
80
年之后
的
2013
年，词向量或“词嵌入”开始成为语言表示的主要方法，如图
7-8
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