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Artificial intelligence for IT operations (AIOps)


Artificial intelligence for IT operations (AIOps) is the use of deep learning and big data analytics to automate routine administrative tasks, including deployment, root cause analysis and problem resolution, for an information technology (IT) system.

Ideally, an AIOps platform brings three important capabilities to the enterprise:


  • The ability to recognize abnormal system behavior faster and with greater accuracy than humanly possible.
  • The ability to use IFTTT business rules to automate routine tasks.
  • The ability to streamline communication among stakeholders.


How AIOps works

AIOps tools gather information from the IT tools and devices already in place and apply detailed analytics and machine learning to that information in order to identify potential issues and correct them. Typically, AIOps data comes from network log files, cloud monitoring tools and helpdesk ticketing systems.

Big data technologies aggregate and organize all of the systems' output into a form that an AIOps platform can use. The platform uses correlation engines and business rules to monitor throughput and either do nothing, take action autonomously or alert a human administrator when required.

AIOps platforms are designed to illustrate dependencies and the role each dependency plays in both normal and abnormal system behavior. To be effective, AIOps tools must be adaptive to machine-learning-specific workflows and be able to handle the recursion required to support continuous machine learning (ML) model training.

Use case for AIOps

Although the underlying technologies for AIOps are relatively mature, it is still an early field in terms of combining the technologies for practical use. Organizations that want to streamline data-intensive, manual and repetitive tasks, such as ticketing, are good candidates for an AIOps platform proof-of-concept project.

Challenges of AIOps

AIOps is only as good as the data it receives and the algorithms that it is taught. The amount of time and effort needed to implement, maintain and manage an AIOps platform can be substantial. The diversity of available data sources as well as proper data storage, protection and retention are all important factors in AIOps results.

AIOps demands trust in tooling, which can be a gating factor for some businesses. For an AIOps tool to act autonomously, it must follow changes within its target environment accurately, gather and secure data, form correct conclusions based on the available algorithms and machine learning, prioritize actions properly and take the appropriate automated actions to match business priorities and objectives.


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