AIOps (artificial intelligence operations) - definition & overview

AIOps, artificial intelligence for IT operations, refers to using artificial intelligence and machine learning to perform and automate tasks normally executed manually by IT operators. Implementations of AIOps use mathematical models that leverage correlation and analysis to set off trigger-based response algorithms that can start subroutines and react based on criteria (parameters) that humans (IT Operators) set up ahead of time.

AIOps represents cutting-edge innovation in IT operations technology, with the term coined by Gartner in a 2017 report. Enterprise organizations have been experiencing unprecedented digital transformation characterized by the widespread implementation of microservices, new technologies managing big data, multi-cloud architecture, migration of on-premise infrastructure to the cloud and rapid innovation.

Digital transformation has seen the large-scale expansion of web-based services in a hybrid cloud environment, creating significant observability challenges for IT operators and analysts charged with application performance monitoring and maintaining the security and operational efficiency of IT systems and user experience.

An effective AIOps platform helps facilitate IT infrastructure monitoring by collecting and aggregating data from the network without human intervention. Data sources include event log files from servers, applications and other network endpoints. Capturing data from multiple previously siloed sources and integrating them into a single database makes it easier for machine learning algorithms to assess network characteristics and performance in real time.

AIOps software can be configured to track specific service-level indicators (SLIs) for a given server or application. IT operators may conduct performance tests to establish a baseline for service level objectives (SLOs) and define acceptable thresholds for the ones they intend to prioritize. When an SLO breach is detected, AIOps software can perform an automated root cause analysis, determining why a problem occurred and implementing a solution, if available, to reduce the mean time to resolution (MTTR).

AIOps software tools support the incident management process by automating incident response to routine alerts, significantly reducing IT operators' time on mundane, low-value tasks. AIOps tools can also feed machine-enriched data directly into the incident management processes, acting as valuable data sources and analyses that drive IT improvements for end users. More recently, generative AI tools promise to significantly increase the value and effectiveness of an AIOps platform by summarizing actionable insights, including predictive analytics, delivering anomaly detection, root cause analysis and automated remediation.

AIOps is best described as a set of technologies that make up a platform rather than a single application. No matter which AIOps platform you may be using, they all use artificial intelligence to support the responsibilities and activities of an IT operations team. The basic components and features of an AIOps software management tool can be summarized as follows:

• Data aggregation
  A core AIOps capability is aggregating data from various sources within DevOps infrastructure, including historical data, event logs, system tracing, apps, job data, tickets and more. Removing data silos makes it easier to maintain oversight of IT infrastructure and correlate events on the network to determine their root cause.

• Real-time processing
  Real-time data processing allows for a balance to be struck between ITOps meeting performance optimization requirements and security analysts managing countermeasures. With artificial intelligence, enterprise IT organizations can effectively ingest and analyze large volumes of data at scale and in real-time. As a result, these organizations can identify anomalies and respond more quickly to security events that are picked up by their AIOps tool.

• Rules and patterns
  Artificial intelligence tools use rule application and pattern recognition algorithms to detect network events that warrant a response. They may even use machine learning algorithms that allow them to develop their own rules for detecting network anomalies based on training data sets. Rules and patterns are used to distinguish between network activity that is considered "normal" and that which is deemed "anomalous” to accelerate decision-making.

• Domain algorithms
  Domain algorithms are specific to an industry or IT environment, and their contents and structure are dictated by an IT organization's unique goals and data. These algorithms define the specific operational goals that will be prioritized by artificial intelligence.

• Artificial intelligence and machine learning
  The defining feature of AIOps. Regarding AIOps technology, artificial intelligence implementations are geared towards the intelligent analysis of large volumes of data and the capability of in-depth analysis via mathematical models that correlate and parse through machine data to produce histograms, charts and visualization.

• Automation
  

  Reducing workload for IT operators is one of the main reasons AIOps tools exist, making automation one of their most important features. AIOps can be used to orchestrate and automate real-time testing of new software features and user stories or to perform in-depth log analysis and detect errors and anomalies.

Sumo Logic is a cloud-native, multi-tenant platform that helps IT teams quickly arrive at data-driven decisions that reduce the time to investigate and remediate security and operational issues. Sumo Logic’s Observability platform is built from the ground up as an integrated portfolio of capabilities for monitoring (what happened), diagnosis (where it happened) and troubleshooting (why it happened) across disparate telemetry and powered by our entity backend. Use Sumo Logic to:

Collect and centralize - more than 175 integrations make aggregating data across the tech stack and down the telemetry pipeline easy. Sumo Logic is working toward a unified collection model that fits the OpenTelemetry standard.

Monitor and visualize - customizable dashboards align teams by visualizing logs, metrics and performance data for full-stack visibility and reliable delivery.

Search and investigate - real-time analytics to rapidly identify and resolve potential issues, detect and prevent breaches, and reduce compliance costs.

Alert and notify - Machine-learning algorithms work 24/7 to send alerts if there’s an important event or problem to fix.

With Sumo Logic's patented artificial intelligence technologies, LogReduce and LogCompare, IT organizations can aggregate large volumes of logs, events, and time-series metrics, identify and predict anomalies in real-time, and deliver crucial security and operational data to where it can be used to guard against data breaches and optimize the customer experience.

Learn more about artificial intelligence for log analytics in our guide.

How can I measure the effectiveness of AIOps implementation?

• Mean time to resolution

• Anomaly detection accuracy

• Incident response time

• Infrastructure monitoring coverage

• Predictive analytics performance.

What operations processes can AIOps help improve?