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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87
第 3 章
自动化机器学习
本章，我们将展示如何使用完全托管的
Amazon AI
和机器学习服务来避免管理我们
自己的
AI
和机器学习流水线基础设施。我们深入了解
Amazon
的自动化机器学习服
务，
Amazon SageMaker Autopilot
和
Amazon Comprehend
，通过这两项服务，用户
只需几次点击，就可以为其数据集构建强大的预测模型。我们可以使用
SageMaker
Autopilot
和
Comprehend
在很少的工作量和很低的开销下建立模型性能的基线。
机器学习从业者一般需要数周或数月时间来构建、训练并调试模型。他们准备数据
并决定使用的框架和算法。在迭代过程中，
ML
从业者尝试为其数据集和问题类型
找到性能最佳的算法。不幸的是，这个过程没有捷径。我们仍需要凭借经验、直觉
和耐心来运行许多实验并为我们的算法和数据集寻找最优超参数。老练的数据科学
家受益于日积月累的经验和直觉，能够为特定数据集和问题类型选择最好的算法，
但他们仍然需要通过实际运行训练和反复检验模型来验证直觉。
如果我们只需要用一个服务，几步点击，就可以为数据集找到最佳算法、训练并调
试模型、最终将模型部署到生产，那会怎么样？
Amazon SageMaker Autopilot
简化
了模型的训练和调试流程并加速了整体的模型开发周期。通过在特征选择、超参数
调试（
hyper-parameter tuning
，
HPT
）这样的样板阶段花费更少的时间，我们就可
以在领域特定的问题上投入更多。
通过分析存储在
S3
上的数据，
SageMaker ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.