October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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周）自动运行。
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编写另一个脚本，该脚本将在更新的测试集上评估新模型和旧模型，如果性能良好，
则将该模型部署到生产环境中（如果性能降低，请调查原因）。
你还应该确保评估模型的输入数据质量。有时由于信号质量较差（例如故障、传感器发
送随机值，或者其他的输出过时了），但是你的系统性能可能需要一段时间才能触发警
报。如果你监控模型的输入，则可能会更早一点发现。例如，越来越多的输入缺少某个
特征，或者其平均值或标准差偏离训练集太远，或者分类特征中出现了新类别。
最后，请保留每个模型的备份，准备好流程和工具，以便在新模型出现时快速回滚到以
前的模型，以防新模型由于某种情况开始出现严重故障。进行备份还可以轻松实现将新
模型与以前的模型进行比较。同样，你应该保留每个版本的数据集，以便在新数据集遭
到破坏的情况下可以回滚到先前的数据集（如果事实能证明添加到其中的新数据都是离
群 值 ）。 备份数据集还可以针对任何先前的数据集来评估任何模型。
你可能需要创建测试集的几个子集，以便评估你的模型在特定部分数据的效
果如何。例如，你可能希望有一个仅包含最新数据，或用于特定类型输入的
测试集（例如，位于内陆的区域与靠近海洋的区域）。这将使你更深入地了
解模型的长处和短处。
如你所见，机器学习涉及很多基础架构，如果你的第一个 ML 项目花费大量精力和时间
来构建和部署到生产环境，请不要觉得奇怪。幸运的是，一旦所有基础设施都准备就
绪，从构想到生产环境将会很快实现。
2.9 试试看
希望通过本章内容，你能够了解一个机器学习项目大概是什么样子，同时了解一些用来 ...
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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.