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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233
第 7 章
训练第一个模型
在第
6
章，我们使用
SageMaker
处理任务，通过“特征工程”这一过程将原始
数据集转换为机器可用的特征。在本章，我们使用这些特征，借助
TensorFlow
、
PyTorch
、
BERT
（
https://oreil.ly/hmyQz
）和
SageMaker
来训练一个定制的评价分类器，
以分类来源于社交渠道、合作网站的“野生（
in the wild
）”评价。我们甚至展示如
何使用
Java
来训练
BERT
模型！
在这一过程中，我们解释诸如
Transformers
架构、
BERT
和调优预训练模型等关键
概念。我们也描述
SageMaker
提供的不同训练选项，包括内建算法和“自带（
bring-
your-own
）”选项。接下来，我们讨论
SageMaker
的基础设施，包括容器、网络和安全。
之后我们使用
SageMaker
训练、评估并分析模型。分析（
Profiling
）能够帮助我们
调试模型、降低训练时长和开销。最后，我们提供技巧在使用
SageMaker
开发模型时，
进一步降低开销并提升性能。
7.1
理解
SageMaker
基础设施
依托容器技术，
SageMaker
管理基础设施并帮助我们专注于特定的机器学习任务。
我们可以直接利用众多开箱即用的内建算法中的一个，应用在诸如自然语言处
理（
NLP
）、分类、回归、计算机视觉和监督学习等场景中。除内建算法之外，
SageMaker
也提供预先构建的容器以支撑许多流行的
AI
和机器学习框架，例如
TensorFlow
，
PyTorch
，
Apache MXNet
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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.