AIops

How does AIOps work?With AIOps, your organization takes a more proactive approach to resolve IT operational issues. Instead of relying upon sequential system alerts, your IT teams use machine learning and big data analytics. This breaks down data silos, improves situational awareness, and automates personalized responses to incidents. With AIOps, your organization is better able to enforce IT policies to support business decisions.Next, we discuss interconnected AIOps phases. ObserveThe observe phase refers to the intelligent collection of data from your IT environment. AIOps improves observability amongst disparate devices and data sources across your organization's network.By deploying big data analytics and ML technologies, you can ingest, aggregate, and analyze massive amounts of information in real time. An IT operations team can identify patterns and correlate events in log and performance data. For example, businesses use AI tools to trace the request path in an API interaction. EngageThe engage phase involves using human experts to resolve issues. Operations teams reduce their dependencies on conventional IT metrics and alerts. They use AIOps analytics to coordinate IT workloads on multicloud environments. IT and operational teams share information with a common dashboard to streamline efforts in diagnosis and assessment.The system also raises personalized and real-time alerts to the appropriate teams. It does this both preemptively and in case of incidents.ActThe act phase refers to how AIOps technologies take actions to improve and maintain IT infrastructure. The eventual goal of AIOps is to automate operational processes and refocus teams' resources on mission-critical tasks.IT teams can create automated responses based on the analytics that ML algorithms generate. They can deploy more intelligent systems that learn from historical events and preempt similar issues with automated scripts. For example, your developers can use AI to automatically inspect codes and confirm problem resolution before they release software updates to affected customers.

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AIOps 如何运作？借助 AIOps，您的组织可以采取更主动的方式来解决 IT 运营问题。您的 IT 团队不再依赖顺序系统警报，而是使用机器学习和大数据分析。这可以打破数据孤岛、提高态势感知能力并自动对事件做出个性化响应。

借助 AIOps，您的组织可以更好地执行 IT 政策来支持业务决策。接下来，我们讨论相互关联的 AIOps 阶段。

观察

观察阶段是指从您的 IT 环境中智能收集数据。AIOps 提高了组织网络中不同设备和数据源之间的可观察性。通过部署大数据分析和 ML 技术，您可以实时提取、汇总和分析大量信息。IT 运营团队可以识别模式并关联日志和性能数据中的事件。例如，企业使用 AI 工具来跟踪 API 交互中的请求路径。

参与

参与阶段涉及使用人类专家来解决问题。运营团队减少了对传统 IT 指标和警报的依赖。他们使用 AIOps 分析来协调多云环境中的 IT 工作负载。IT 和运营团队通过通用仪表板共享信息，以简化诊断和评估工作。该系统还向相应的团队发出个性化和实时警报。它既可以预防性地这样做，也可以在发生事故时这样做。

行动

行动阶段是指 AIOps 技术如何采取行动来改善和维护 IT 基础设施。AIOps 的最终目标是实现运营流程自动化，并将团队的资源重新集中在关键任务上。IT 团队可以根据 ML 算法生成的分析创建自动响应。他们可以部署更智能的系统，从历史事件中学习，并使用自动化脚本预先解决类似问题。例如，您的开发人员可以使用 AI 自动检查代码并确认问题已解决，然后再向受影响的客户发布软件更新。