[英]AWS SageMaker Random Cut Forest or Kinesis Data Analytics Random Cut Forest?
I need to put together an architecture that can detect anomalies in logs created by a web application. 我需要建立一个可以检测Web应用程序创建的日志中异常的体系结构。
The Random Cut Forest algorithm constantly pops up in my research, where it is used in two scenarios: SageMaker and Kinesis Data Analytics. 我的研究不断弹出“随机砍伐森林”算法,该算法在两种情况下使用:SageMaker和Kinesis Data Analytics。
Which of these two services should I use in my architecture? 我应该在体系结构中使用这两项服务中的哪一项?
At the core, the mathematical methodology between the two is nearly identical, but there are some differences in how they are implemented within Kinesis and SageMaker that should help drive your decision. 从根本上说,两者之间的数学方法几乎完全相同,但是在Kinesis和SageMaker中如何实现它们方面存在一些差异,这应该有助于您做出决定。
Kinesis RandomCutForest: Kinesis RandomCutForest:
SageMaker RandomCutForest: SageMaker RandomCutForest:
Hope this answers your question. 希望这能回答您的问题。
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