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# awesome-AIOps
[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
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- [Awesome AIOps](#awesome-AIOps)
    - [White Paper](#white-paper)
    - [Course and Slides](#course-and-slides)
    - [Industry Practice](#industry-practice)
    - [Article](#article)
    - [Tools and Algorithms](#tools-and-algorithms)
    - [Paper](#paper)
    - [Dataset](#dataset)
    - [Useful WeChat Official Accounts](#useful-wechat-official-accounts)
    
## White Paper
* [《企业级 AIOps 实施建议》白皮书](https://www.rizhiyi.com/assets/docs/AIOps.pdf)

## Course and Slides
* [Tsinghua-Peidan](http://netman.ai/courses/advanced-network-management-spring2018-syllabus/) - AIOps course in Tsinghua.
* [基于机器学习的智能运维](http://netman.ai/wp-content/uploads/2016/12/%E5%9F%BA%E4%BA%8E%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9A%84%E6%99%BA%E8%83%BD%E8%BF%90%E7%BB%B4v1.6.pdf)

## Industry Practice
-------------------------------------------------------------------------------
* [腾讯运维的AI实践](https://myslide.cn/slides/8935)
* [AI 时代下腾讯的海量业务智能监控实践](https://cloud.tencent.com/developer/article/1039354)
* [织云Metis时间序列异常检测全方位解析](https://ppt.geekbang.org/slide/show?cid=30&pid=1595)
* [腾讯织云Metis智能运维学件平台开源代码](https://github.com/Tencent/Metis)
-------------------------------------------------------------------------------
* [阿里全链路监控方案](https://mp.weixin.qq.com/s/DJhJKD4TCDgSwyLZbSotKg)
* [阿里开源4000台服务器真实数据集](https://github.com/alibaba/clusterdata/tree/v2018)
-------------------------------------------------------------------------------
* [百度智能流量监控实战](https://ppt.geekbang.org/slide/show?cid=30&pid=1548)
* [异常检测：百度是这样做的](https://mp.weixin.qq.com/s/AXhjawsINKl6cLDV1yf6fw)
* [Next Generation of DevOps AIOps in Practice @Baidu](https://www.usenix.org/sites/default/files/conference/protected-files/srecon17asia_slides_qu.pdf) [[video]](https://www.youtube.com/watch?v=5YfqevEtIFw)
-------------------------------------------------------------------------------
* [搭建大规模高性能的时间序列大数据平台](https://ppt.geekbang.org/list/assz2018)
* [Yahoo大规模时列数据异常检测技术及其高性能可伸缩架构](http://www.infoq.com/cn/articles/automated-time-series-anomaly-detection?utm_source=articles_about_bigdata&utm_medium=link&utm_campaign=bigdata)
* [Netflix: Robust PCA](https://medium.com/netflix-techblog/rad-outlier-detection-on-big-data-d6b0494371cc)
* [LinkedIn: exponential smoothing](https://github.com/linkedin/luminol)
* [Uber: multivariate non-linear model](https://eng.uber.com/argos/)

## Article
* [智能运维|AIOps中的四大金刚都是谁？](https://mp.weixin.qq.com/s/NKhQkS59WIGgbIfFKcxonA)
* [A Comparison of Mapping Approaches for Distributed Cloud Applications](https://blog.netsil.com/a-comparison-of-mapping-approaches-for-distributed-cloud-applications-52be1f61d293)
* [AIOps探索：基于VAE模型的周期性KPI异常检测方法](https://zhuanlan.zhihu.com/p/45400663)

## Tools and Algorithms
* [Tools to Monitor and Visualize Microservices Architecture](https://www.programmableweb.com/news/tools-to-monitor-and-visualize-microservices-architecture/analysis/2016/12/14)
* [python-fp-growth，挖掘频繁项集](https://github.com/enaeseth/python-fp-growth)
* [Anomaly Detection with Twitter in R](https://github.com/twitter/AnomalyDetection)
* [百度开源时间序列打标工具：Curve](https://github.com/baidu/Curve)
* [Microsoft开源时间序列打标工具: TagAnomaly](https://github.com/Microsoft/TagAnomaly)
* [Anomaly Detection Examples](https://github.com/shubhomoydas/ad_examples)
* [facebook/prophet, Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.](https://facebook.github.io/prophet)
* [google/CausalImpact, An R package for causal inference in time series](https://github.com/google/CausalImpact)
* [时间序列分析之ARIMA](https://blog.csdn.net/u010414589/article/details/49622625)
* [时间序列特征提取库tsfresh](https://github.com/blue-yonder/tsfresh)
* [Yahoo EGADS : A Java package to automatically detect anomalies in large scale time-series data](https://github.com/yahoo/egads)
* [Awesome Time Series Analysis and Data Mining](https://github.com/youngdou/awesome-time-series-analysis)

## Paper
* [Survey on Models and Techniques for Root-Cause Analysis](https://arxiv.org/pdf/1701.08546.pdf)
* [基于机器学习的智能运维](http://netman.ai/wp-content/uploads/2018/04/peidan.pdf)
* [HotSpot: Anomaly Localization for Additive KPIs With Multi-Dimensional Attributes](http://netman.ai/wp-content/uploads/2018/03/sunyq_IEEEAccess_HotSpot.pdf)
    * Chinese：[清华AIOps新作:蒙特卡洛树搜索定位多维指标异常](https://mp.weixin.qq.com/s/Kj309bzifIv4j80nZbGVZw)
* [Opprentice: Towards Practical and Automatic Anomaly Detection Through Machine Learning](http://conferences2.sigcomm.org/imc/2015/papers/p211.pdf)
* [Robust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection](http://netman.ai/~peidan/ANM2018/8.DependencyDiscovery/LectureCoverage/2018IWQOS_ROCKA.pdf)

## Dataset
* [Alibaba/clusterdata](https://github.com/alibaba/clusterdata)
* [Azure/AzurePublicDataset](https://github.com/Azure/AzurePublicDataset)
* [Google/cluster-data](https://github.com/google/cluster-data)
* [The Numenta Anomaly Benchmark(NAB)](https://github.com/numenta/NAB)
* [Yahoo: A Labeled Anomaly Detection Dataset](https://webscope.sandbox.yahoo.com/catalog.php?datatype=s&did=70)
* [港中文loghub数据集](https://github.com/logpai/loghub)

## Useful WeChat Official Accounts
* 腾讯织云（腾讯的）
* 智能运维前沿（清华裴丹团队的）
* AIOps智能运维（百度的）
* 华为产品可服务能力（华为的）
* 知乎专栏：[智能运维(AIOps)](https://zhuanlan.zhihu.com/awesome-AIOps)

一键复制编辑原始数据按行查看历史

提交于 2019-02-25 23:54 . Update README.md

awesome-AIOps

Awesome AIOps

White Paper

《企业级 AIOps 实施建议》白皮书

Course and Slides

Tsinghua-Peidan - AIOps course in Tsinghua.
基于机器学习的智能运维

Industry Practice

Article

Tools and Algorithms

Paper

Dataset

Useful WeChat Official Accounts

腾讯织云（腾讯的）
智能运维前沿（清华裴丹团队的）
AIOps智能运维（百度的）
华为产品可服务能力（华为的）
知乎专栏：智能运维(AIOps)

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