July 2023
Beginner to intermediate
270 pages
4h 48m
Chinese
Content preview from 可观测性工程
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84
|
第
8
章
这就引出了一个问题，即应该对这个数字运算应用多少智能。人工智能和机器学习是我
们所有调试问题的必然解决方案吗？
8.3 AIOps 的误导性承诺
自
Gartner
于
2017
年创造该术语以来，
AIOps
（
Artifical Intelligence for Operations
，智
能运维）引起了寻求以某种方式自动化常见运营任务的公司的极大兴趣。深入研究
AIOps
的误导性承诺超出了本书的范围。但是
AIOps
与可观测性相交，减少了告警噪声
和异常检测。
在第
12
章，我们将分析一种更简单的告警方法，它使减少告警噪声的算法成为一个有
争议的问题。第二个交叉点
—
异常检测
—
则是本章的重点，因此我们将在本节展开
介绍。
如本章前面所述，使用算法进行异常检测，基本的概念是选择“正常”事件的基线，并
将其与基线窗口中未包含的“异常”事件进行比较。对于自动化算法而言，选择完成该
操作的窗口可能极具挑战性。
类似于使用
BubbleUp
在你关心的区域周围绘制一个框，
AI
必须决定在哪里绘制自己的
框。如果一个系统随着时间的推移表现一致，那么异常将是令人担忧的、很容易检测到
的不寻常情况。在系统行为频繁变化的创新环境中，人工智能更有可能绘制大小错误的
框。该框要么太小，将大量完全正常的行为识别为异常；要么太大，将异常错误归类为
正常行为。在实践中，两种类型的错误都会发生，并且检测会过于嘈杂或过于安静。
本书是关于在现代架构上，用现代实践管理生产环境所需的工程原则。当今世界上任何
具有相当竞争力的公司都会有工程团队经常将变更部署到生产环境中。新功能的部署引 ...
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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.