- 具备使用监控系统（如Prometheus或ELK）的经验
- 具备Python的基础知识以及基本的机器学习技能
- 熟悉事件管理工作流程
受众
- 高级站点可靠性工程师（SREs）
- IT自动化架构师
- DevOps与可观测性平台负责人
AIOps（IT运维人工智能）正越来越多地用于预测事件发生前的故障，并自动化根本原因分析（RCA），以最小化停机时间并加速问题解决。
本课程为讲师引导的培训（线上或线下），面向希望使用AIOps工具和机器学习模型实现预测分析、自动化修复并设计智能RCA工作流程的高级IT专业人士。
在本培训结束时，参与者将能够：
- 构建和训练机器学习模型，以检测导致系统故障的模式。
- 基于多源日志和指标相关性，自动化RCA工作流程。
- 将告警和修复流程集成到现有平台中。
- 在生产环境中部署和扩展智能AIOps管道。
课程形式
- 互动讲座与讨论。
- 大量练习与实践。
- 在实时实验室环境中进行实际操作。
课程定制选项
- 如需为本课程定制培训，请联系我们进行安排。
Predictive AIOps 简介
- IT 运维中的预测分析概述
- 用于预测的数据来源（日志、指标、事件）
- 时间序列预测和异常模式的关键概念
设计事件预测模型
- 标记历史事件和系统行为
- 选择和训练模型（例如 LSTM、Random Forest、AutoML）
- 评估模型性能和处理误报
数据收集与特征工程
- 摄取和对齐日志与指标数据以供模型输入
- 从结构化和非结构化数据中提取特征
- 处理操作管道中的噪声和缺失数据
自动化根因分析（RCA）
- 基于图的服务与基础设施关联
- 使用机器学习从事件链中推断可能的根因
- 使用拓扑感知仪表板可视化 RCA
修复与 Workflow Automation
- 与自动化平台集成（例如 Ansible、Rundeck）
- 触发回滚、重启或流量重定向
- 审核和记录自动化干预
扩展智能 AIOps 管道
- MLOps 用于可观测性：重新训练和模型版本控制
- 在分布式节点上实时运行预测
- 在生产环境中部署 AIOps 的最佳实践
案例研究与实际应用
- 使用预测 AIOps 模型分析真实事件数据
- 使用合成和生产数据部署 RCA 管道
- 行业用例回顾：云中断、微服务不稳定、网络退化
总结与后续步骤
United Arab Emirates - AIOps in Action: Incident Prediction and Root Cause Automation
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