本文转载自GitHub: https://github.com/linjinjin123/awesome-AIOps
White Paper
Course and Slides
- Tsinghua-Peidan - AIOps course in Tsinghua.
- 基于机器学习的智能运维
Industry Practice
- 搭建大规模高性能的时间序列大数据平台
- Yahoo大规模时列数据异常检测技术及其高性能可伸缩架构
- Netflix: Robust PCA
- LinkedIn: exponential smoothing
- Uber: multivariate non-linear model
Article
- 智能运维|AIOps中的四大金刚都是谁？
- A Comparison of Mapping Approaches for Distributed Cloud Applications
- AIOps探索：基于VAE模型的周期性KPI异常检测方法
Tools and Algorithms
- Tools to Monitor and Visualize Microservices Architecture
- python-fp-growth，挖掘频繁项集
- Anomaly Detection with Twitter in R
- 百度开源时间序列打标工具：Curve
- Microsoft开源时间序列打标工具: TagAnomaly
- Anomaly Detection Examples
- facebook/prophet, Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
- google/CausalImpact, An R package for causal inference in time series
- 时间序列分析之ARIMA
- 时间序列特征提取库tsfresh
- Awesome Time Series Analysis and Data Mining
Paper
- Survey on Models and Techniques for Root-Cause Analysis
- 基于机器学习的智能运维
- HotSpot: Anomaly Localization for Additive KPIs With Multi-Dimensional Attributes
- Chinese：清华AIOps新作:蒙特卡洛树搜索定位多维指标异常
- Opprentice: Towards Practical and Automatic Anomaly Detection Through Machine Learning
- Robust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection
Dataset
- Alibaba/clusterdata
- Azure/AzurePublicDataset
- Google/cluster-data
- The Numenta Anomaly Benchmark(NAB)
- Yahoo: A Labeled Anomaly Detection Dataset
- 港中文loghub数据集
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)
特别申明：该文章转自于 github ，感谢linjinjin123的汇总
White Paper
Course and Slides
- Tsinghua-Peidan - AIOps course in Tsinghua.
- 基于机器学习的智能运维
Industry Practice
- 搭建大规模高性能的时间序列大数据平台
- Yahoo大规模时列数据异常检测技术及其高性能可伸缩架构
- Netflix: Robust PCA
- LinkedIn: exponential smoothing
- Uber: multivariate non-linear model
Article
- 智能运维|AIOps中的四大金刚都是谁？
- A Comparison of Mapping Approaches for Distributed Cloud Applications
- AIOps探索：基于VAE模型的周期性KPI异常检测方法
Tools and Algorithms
- Tools to Monitor and Visualize Microservices Architecture
- python-fp-growth，挖掘频繁项集
- Anomaly Detection with Twitter in R
- 百度开源时间序列打标工具：Curve
- Microsoft开源时间序列打标工具: TagAnomaly
- Anomaly Detection Examples
- facebook/prophet, Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
- google/CausalImpact, An R package for causal inference in time series
- 时间序列分析之ARIMA
- 时间序列特征提取库tsfresh
- Awesome Time Series Analysis and Data Mining
Paper
- Survey on Models and Techniques for Root-Cause Analysis
- 基于机器学习的智能运维
- HotSpot: Anomaly Localization for Additive KPIs With Multi-Dimensional Attributes
- Chinese：清华AIOps新作:蒙特卡洛树搜索定位多维指标异常
- Opprentice: Towards Practical and Automatic Anomaly Detection Through Machine Learning
- Robust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection
Dataset
- Alibaba/clusterdata
- Azure/AzurePublicDataset
- Google/cluster-data
- The Numenta Anomaly Benchmark(NAB)
- Yahoo: A Labeled Anomaly Detection Dataset
- 港中文loghub数据集
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)
特别申明：该文章转自于 github ，感谢linjinjin123的汇总