AIOps (artificial intelligence for IT operations)

By Kinza Yasar

What is AIOps (artificial intelligence for IT operations)?

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:

  1. 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.
  2. 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.
  3. 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Key AIOps use cases

AIOps is generally used in companies that also use DevOps or cloud computing as well as in large, complex enterprises. AIOps aids teams that use a DevOps model by giving them additional insight into their IT environment and high volumes of data, which then gives the operations teams more visibility into changes in production.

Some common use cases for AIOps include the following:

AIOps technologies

AIOps uses a conglomeration of various AI strategies, including data output, aggregation, advanced analytics, algorithms, automation and orchestration, machine learning, and visualization. Most of these technologies are reasonably well defined and mature.

AIOps benefits and drawbacks

AIOps comes with the following advantages and disadvantages:

Benefits of AIOps

Drawbacks of AIOps

AIOps vendors

To demonstrate value and mitigate risk from AIOps deployment, organizations should introduce the technology in small, carefully orchestrated phases. They should decide on the appropriate hosting model for the tool, such as on site or as a service. IT staff must understand and then train the system to suit the organization's needs, and to do so must have ample data from the systems under its watch.

AIOps is an emerging area, but there's a growing stable of product offerings for businesses to review and evaluate, including but not limited to the following:

Future of AIOps

The future of AIOps looks promising. According to a report from The Insight Partners, the global AIOps platform market is predicted to increase at a compound annual growth rate from $2.83 billion in 2021 to $19.93 billion by 2028.

AIOps is expected to assist enterprises in enhancing their IT operations by minimizing noise, facilitating collaboration, offering full visibility and boosting IT service management. The AIOps technology has the potential to facilitate digital transformation by providing enterprises with a more agile, flexible and secure IT infrastructure. In addition, it's expected to mature and gain market acceptance, with enterprises incorporating it into their DevOps initiatives to automate infrastructure operations.

Interest in AIOps and observability is growing exponentially in IT, but it doesn't come without its adoption challenges. Learn how to overcome AIOps adoption barriers and get visibility into problem areas for enhanced operations.

01 Jun 2023

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