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
｜
21
AutoML
不仅限于
SageMaker
。很多
Amazon AI
服务都使用
AutoML
来
为已有数据集寻找最佳模型和超参数。
AutoML
通常指为自动化前述模型开发过程中的典型步骤而付出的努力。
Amazon
SageMaker Autopilot
是一个将
AutoML
应用到我们数据集上的完全托管服务。
SageMaker Autopilot
首先分析我们的表格数据，识别机器学习问题的类型（回归、
分类等）并选择算法（
XGBoost
等）来解决问题，同时创建所需的数据转换代码来
为模型训练进行数据预处理。之后，
Autopilot
创建一些代表不同数据转换和算法选
择的候选机器学习模型流水线。
Autopilot
首先在特征工程步骤中应用数据转换，然
后训练并微调这些候选模型。训练结果是基于像验证准确性这样的预定义的目标指
标而产生的候选模型排名列表（排行榜）。
SageMaker
是透明
AutoML
的一个例子。
Autopilot
不仅与我们共享数据转换代码，
而且生成额外的
Jupyter notebooks
来记载数据分析步骤的结果和用于重现模型训练
过程的候选模型流水线。
我们可以在很多场景下使用
SageMaker Autopilot
。我们可以赋予我们组织中的更
多人，如机器学习经验有限的软件开发者，以构建模型的能力。我们可以自动化易
解机器学习问题的模型创建，这让我们能够专注在新的复杂使用场景上。我们可以
自动化数据分析和准备的一些最初步骤并以这些结果为基准，引入我们的领域知
识和经验来按需调整和进一步改进模型 ...
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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.