Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning (ML) and other AI technologies to automate the identification and resolution of common IT issues. The systems, services and applications in a large enterprise — especially with the advances in distributed architectures such as containers, microservices and multi-cloud environments — produce immense volumes of log and performance data that can impede an IT team’s ability to identify and resolve incidents. AIOps uses this data to monitor assets and gain visibility into dependencies within and outside of IT systems.
An AIOps platform should provide enterprises with the ability to do the following:
- Automate routine practices. Routine practices include user requests as well as noncritical IT system alerts. For example, AIOps can enable a help desk system to process and fulfill a user request to provision a resource automatically. AIOps platforms can also evaluate an alert and determine that it doesn’t require action because the relevant metrics and supporting data available are within normal parameters.
- Recognize serious issues faster and with greater accuracy than humans. IT professionals might address a known malware event on a noncritical system, but ignore an unusual download or process starting on a critical server because they aren’t watching for this threat. AIOps addresses this scenario differently: prioritizing the event on the critical system as a possible attack or infection because the behavior is out of the norm, and deprioritizing the known malware event by running an antimalware function.
- Streamline interactions between data center groups and teams. AIOps provides each functional IT group with relevant data and perspectives. Without AI-enabled operations, such as monitoring, automation and service desk, teams must share, parse and process information by meeting or manually sending around data. AIOps should learn what analysis and monitoring data to show each group or team from the large pool of resource metrics.
How does AIOps work?
AIOps uses advanced analytical technologies such as machine learning to automate and optimize IT operations processes. AIOps typically works by following these steps:
- Data collection. AIOps platforms collect information from a variety of sources, including application logs, event data, configuration data, incidents, performance metrics and network traffic. This data can be both structured, such as databases, or unstructured, such as social media posts and documents.
- Data analysis. The gathered data is analyzed using ML algorithms such as anomaly detection, pattern detection and predictive analytics to find abnormalities that might require the attention of IT staff. This step ensures real issues are separated from noise or false alarms.
- Inference and root cause analysis. AIOps carries out root cause analysis to assist in locating the origins of problems. IT operations teams can attempt to prevent the recurrence of problems in the future by looking into the root causes of current issues.
- Collaboration. Once the root cause analysis is complete, AIOps notifies the appropriate teams and individuals, providing them with relevant information and promoting efficient collaboration despite the potential geographical distance between them. In addition, this partnership helps to preserve event data that could be essential for identifying future issues of a similar nature.
- Automated remediation. AIOps can remediate issues automatically, significantly reducing manual intervention and speeding up incident response. These can be automated responses, such as resource scaling, restarting a service or executing predefined scripts to address problems.