What is AIOps

Artificial Intelligence for IT Operations (AIOps) AIOps tools involve using Artificial Intelligence and Machine Learning technologies along with big data, data integration, and automation technologies to help make IT operations more smarter and predictive. AIOps complement manual operations with machine-driven decisions.

Types of AIOps Tools

AIOps solutions are categorized into two areas: 1) Domain-centric and 2) 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-centric, bringing the power of AI to the domains they manage.

Domain-agnostic solutions operate more broadly and work across domains, monitoring, logging, cloud, infrastructure, etc., These tools operate on vast amounts of IT data ingested from all domains/tools and they come up with models from this data to provide more accurate inferences and decisions.

AIOps Platform Enabling Continuous Insights Across IT Operations Monitoring
AIOps Illustration. Source: Gartner

Data Quality and Completeness

The success of AIOps depends on the quality and completeness of data that you provide to the tool, 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

Data Preparation & Integration

It is also essential to effectively ingest, prepare or transform data and make it consumable and ready for AIOps. Some of the key challenges in data preparation & data integration activities, when implementing AIOps projects.

Different environments (edge/on-prem/cloud), data formats (text/binary/JSON/XML/CSV), data delivery modes (streaming, batch, bulk, notifications), programmatic interfaces (APIs/Webhooks/Queries/CLIs). Complex data preparation activities involving integrity checks, cleaning, transforming, and shaping the data (aggregating/filtering/sorting).

Doing these activities manually is not optimal, increases cost and delays ROI. A modern way to do this is to use data bots that can completely automate all such data tasks in AIOps.

Data Enrichment

AIOps solutions need 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 AIOps Use Cases:

Some common use cases or problem areas that can be solved with AIOps are:

  1. Identifying problems based on anomalies or deviations from normal behavior
  2. Forecasting value of a certain metric to prevent outages or to improve operational readiness
  3. Grouping or clustering alerts, events or logs based on symptoms or text descriptions
  4. Correlating events to reduce noise in IT data and extract actionable events
  5. Deriving application or server health based on multiple sensors or telemetry data
  6. Identifying correlated time series metrics or symptoms for faster root cause inference
  7. Finding similar incidents to accelerate incident resolution
  8. Named entity recognition to enrich incidents for faster processing of incidents
  9. Predicting Incident assignment group based on incident attributes
  10. Incident classification using natural language processing, can also use external service like IBM Watson NLU, OpenAI/GPT-3

AIOps Goals and Key Benefits

The ultimate goal of AIOps is to enable IT transformation and let IT run in Autonomous Operations mode. With AIOps tools, IT organizations 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.

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