What is AIOps or Artificial Intelligence for IT Operations? Top 10 AIOps Use Cases
AIOps involves using AI and ML technologies along with big data, data integration, and automation technologies to help make IT operations smarter and more predictive.
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What is AIOps
Artificial Intelligence for IT Operations (AIOps) involves using Artificial Intelligence and Machine Learning technologies along with big data, data integration, and automation technologies to help make IT operations smarter and more predictive. AIOps complement manual operations with machine-driven decisions.
Types of AIOps Solutions
At a high level, AIOps solutions are categorized into two areas: domain-centric and domain-agnostic, as defined by Gartner. Domain-centric solutions apply AIOps for a certain domain like network monitoring, log monitoring, application monitoring, or log collection. You will often see monitoring vendors claim AIOps but primarily they are domain-agnostic, bringing the power of AI to the domain they manage. Domain-agnostic solutions operate more broadly and work across domains, monitoring, logging, cloud, infrastructure, etc., and they take data from all domains/tools and learn from this data to more accurately establishing patterns and inferences.
Data Quality and Completeness
The success of AIOps depends on the quality and completeness of data that you provide to the solution, and the more complete the data is the better it can learn from patterns and provide inferences. If you have IT performance visibility gaps, it is first recommended to fill those gaps with a modern monitoring or observability solution like CloudFabrix Observability in a Box.
It is also essential for AIOps solutions to have an understanding of how application services and assets are related to each other so that when alerts or events arise, the tool can take into consideration these relationships to more accurately drive correlations or root cause inferences. Most implementations depend on manual or external data to feed this data to AIOps, which becomes more of a burden and becomes expensive over time to implement and maintain.
Some modern AIOps tools (like CloudFabrix) are quite good at actually discovering and establishing their application/service contextual topology by themselves and optionally they can also integrate with CMDB or IT Asset Management systems (ITAM), to use these tools either for seed context or for the automated periodic data feed.
Top 10 Common AIOps Use Cases
Some common use cases or problem areas that can be solved with AIOps are:
- Identifying problems based on anomalies or deviations from normal behavior.
- Forecasting the value of a certain metric to prevent outages or to improve operational readiness.
- Grouping or clustering alerts, events, or logs based on symptoms or text descriptions.
- Grouping of relatable alerts based on topology or alert attributes.
- Deriving application or server health based on multiple sensors or telemetry data.
- Identifying correlated time-series metrics or symptoms for faster root cause inference.
- Finding similar incidents to accelerate incident resolution.
- Named entity recognition to enrich incidents for faster processing of incidents.
- Predicting Incident assignment group based on incident attributes.
- Incident classification using natural language processing can also use external services like OpenAI/GPT-3.
AIOps Goals and Key Benefits
The ultimate goal of AIOps is to enable IT transformation, smarter and predictive operations. With AIOps tools IT organization gain unified event intelligence, reduce noise in IT data and eliminate toil, reduce IT ticket volume, resolve IT problems faster, predict/prevent outages before customer impact, automate root cause analysis, accelerate incident or problem resolution, improve IT productivity, and reduce TCO.
Published at DZone with permission of Tejo Prayaga. See the original article here.
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