Popular GitHub Repositories for Aiops

利用AI大模型，一键解说并剪辑视频； Using AI models to automatically provide commentary and edit videos with a single click.

An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

The open-source AIOps and alert management platform

AIOps学习资料汇总，欢迎一起补全这个仓库，欢迎star

A machine learning toolkit for log-based anomaly detection [ISSRE'16]

An intelligent assistant serving the entire software development lifecycle, powered by a Multi-Agent Framework, working with DevOps Toolkits, Code&Doc Repo RAG, etc.

Your 24/7 On-Call AI Agent - Solve Alerts Faster with Automatic Correlations, Investigations, and More

? llm.report is an open-source logging and analytics platform for OpenAI: Log your ChatGPT API requests, analyze costs, and improve your prompts.

The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.

LogAI - An open-source library for log analytics and intelligence

Easy-to-Use RAG Framework; CCF AIOps International Challenge 2024 Top3 Solution; CCF AIOps 国际挑战赛 2024 季军方案

OpenTelemetry Instrumentation for AI Observability

log anomaly detection toolkit including DeepLog

The fundamental package for AIops with python.

Open-source AI copilot that lets you chat with your observability data and code ??♂?

Log Anomaly Detection - Machine learning to detect abnormal events logs

收集AIOPS（智能运维），时间序列，异常检测，关联分析，告警收敛，根因分析，数据挖掘，机器学习，深度学习的学习资源。欢迎star。Collect learning resources for AIOPS (Intelligent Operation and Maintenance), time series, anomaly detection, correlation analysis, alarm convergence, root cause analysis, data mining, machine learning, and deep learning. Welcome star.

OpsPilot is an open source intelligent operation and maintenance assistant based on deep learning and LLM technology developed by the WeOps team. OpsPilot是WeOps团队开源的一个基于深度学习与LLM技术的智能运维助理

Profile and monitor your ML data pipeline end-to-end

Log-based impactful problem identification using machine learning [FSE'18]