What is AIOPs – Artificial Intelligence for IT Operations

(Last Updated On: April 2, 2020)

The Artificial intelligence for IT Operations or AIOps refers to multi-layered platforms applied to automate and enhance IT operations by the use of big data analytics, machine learning, and other artificial intelligence technologies.

What is AIOPs – Artificial Intelligence for IT Operations

Together they produce immense volumes of log and performance data, which can be used in AIOps to monitor assets and gain visibility into dependency without and outside of IT systems.

robot women in technology background

A useful AIOps is one which brings following three capabilities to an organization:

Automate regularly conducted practices

Automating routine practices includes user requests as well as non-critical IT system alerts. For instance, an AIOps platform can enable a help desk system to process and execute users’ requests to automatically provision a resource. It can assess alerts and conclude that it needs taking no action because related to metric and supporting data are already within normal parameters.

Spotting critical issues rapidly

It’s easy for IT professionals to address a malware attack on a noncritical system, but there should be no unusual downloads or an unwanted process triggered on critical servers because they weren’t being watched.

Here AIOps’ approach is a bit different. It prioritizes the events which are more prone to attack or infections, and de-prioritizing those events which are less likely to meet a threat. This makes a system more responsive to any attack because the system knows which events are likely to receive more attacks than others.

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Streamline the interactions between data center groups and teams.

Typically, the functional IT team members share, parse, and process information with each other manually ad by physically meetings. But can AIOps streamline the communication and provide the IT team with relevant data and perspectives. An AIOps system can learn that what monitoring data and analysis need to be showed to the each group of a team.

AIOps technologies

As mentioned above, AIOps uses the conglomeration of several AI technologies, big data analytics, and machine learning capabilities. Today, all of these technologies are reasonably well mature which makes an AIOps solution more reliable.

Data for AIOps comes from log files, various metrics, and monitoring tools, helpdesk ticking systems, and other sources. Outputs from all these systems or sources are aggregated and organized into useful form with the help of Big Data technologies.

Then we have analytics Techniques that can be used to interpret raw information to create new data and metadata. Analytics also decreases noise caused by unnecessary data. Analytics also uses algorithms to organize a business’ IT expertise, policies, and goals.

With algorithms, AIOps is able to deliver the most sought-after actions or results. How IT personals or team prioritize security-related events or tech app performance decision to a platform is all about algorithms.

With algorithms comes machine learning in action. So, there are several different technologies, which are used in an AIOps and, they together can come up as a single solution.

AIOps benefits:

  • A properly implemented AIOps platform reduces IT staff’s time and attention on mundane, routine, and everyday alerts.
  • AIOps systems also perform the nonstop monitoring of processes, without any relax.
  • It can also observe casual relationships over multiple systems, services, and resources.
  • With machine learning capabilities, AIOps can perform powerful root cause analysis.
  • It can help in improving collaboration and workflow activities among IT groups or business units.
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There may be tons of other benefits which may vary from case to case.

Implementing AIOps

The AIOPs technology should be implemented in small and carefully organized phases to observer their real worth and avoid risk. Also, carefully decide between on-site and or as a service hosted models. It’s the responsibility of IT staff to properly understand the system and then trait it to meet the needs. Of course, the entire operation will be led by an ample amount of data.

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