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The name Metis is taken from the Greek goddess of wisdom, Metis, which is a collection of application practices in the AIOps field. It mainly solves the problem of intelligent operation and maintenance in terms of quality, efficiency and cost. The current version of the open source time series anomaly detection learnware is to solve the anomaly detection problem of time series data from the perspective of machine learning.
The realization of the time series anomaly detection learnware is based on statistical judgment, unsupervised and supervised learning to jointly detect time series data. The first-level decision is made by statistical judgment and unsupervised algorithm, and the suspected abnormality is output. Secondly, the supervised model is judged, and the final test result is obtained. The detection model is generated through a large number of sample training and can be continuously updated according to the sample.
The time series anomaly detection learnware has been covered in 20w+ Zhiyun server, which carries the abnormality detection of 240w+ service indicators. After extensive monitoring and data polishing, the learnware has a wide range of applications in the field of anomaly detection and operation and maintenance monitoring.
The operating system platform currently running is as follows:
- OS: Linux
The development languages supported by the front and back ends are as follows:
- Front-end: JavaScript, TypeScript
- Back-end: Python 2.7
- When installing for the first time, please refer to the installation documentation: install.md
Metis is based on the BSD 3-Clause License, see for details: LICENSE.TXT.
If you find a problem during use, please submit and describe via https://github.com/Tencent/Metis/issues , you can also view other issues here and contribute code by resolving these issues.
If you are contributing your code for the first time, please read CONTRIBUTING to learn about our contribution process.
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QQ technology exchange group: 288723616.
