# linjinjin123/awesome-AIOps

AIOps学习资料汇总，欢迎一起补全这个仓库，欢迎star

Repository: https://github.com/linjinjin123/awesome-AIOps
Canonical: https://ross.abutalabs.com/products/awesome-aiops
License Family: other
Topics: aiops, root-cause-analysis, time-series-analysis, machine-learning, deep-learning, alarm-reduction, anomaly-detection
Last push: 2024-05-27T10:18:48+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2959, "days_push": 828, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1750, forks 382 (observed 2026-08-28T04:05:31.119792+00:00)

## What it is
A curated awesome-list of AIOps (Artificial Intelligence for IT Operations) learning resources, including white papers, courses, industry practices, papers, tools, and datasets. It aggregates materials on anomaly detection, root-cause analysis, time-series analysis, and alarm reduction from companies like Tencent, Alibaba, Baidu, and Netflix.

## Use cases
- find learning materials for AIOps
- learn anomaly detection for time-series monitoring
- research root-cause analysis techniques
- find datasets for intelligent operations research
- discover industry AIOps case studies
- get started with machine learning for IT operations

## When to choose
- you want a curated starting point for AIOps learning
- you need papers, courses, and datasets on anomaly detection or root-cause analysis
- you want industry case studies from major tech companies

## When to avoid
- you need production-ready AIOps software rather than a resource list
- you need a maintained tool or library with a code license

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, monitoring, analytics
- domain: artificial-intelligence, machine-learning, monitoring, awesome-lists
- platform: cross-platform
- tags: aiops, awesome-list, anomaly-detection, root-cause-analysis, time-series, alarm-reduction, curated-resources, devops

## Member repositories
- linjinjin123/awesome-AIOps (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:31.119792+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:28:45.123851+00:00, confidence not recorded.
  - readme: https://github.com/linjinjin123/awesome-AIOps (fetched 2026-08-28T04:05:31.119792+00:00, sha 3b641ad68be9)
- Data as of 2026-08-30T08:39:29.467469+00:00.