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AIOps, "artificial intelligence for IT operations," refers to the use of artificial intelligence and machine learning to perform and automate tasks that would normally be 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 having been coined by Gartner in a 2017 report. According to that report, enterprise organizations had been experiencing an unprecedented period of 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 yielded many positive benefits for enterprise organizations, including reduced service delivery costs, reduced costs associated with accessing and scaling IT infrastructure and increased availability of data storage and computing power on an on-demand basis. At the same time, the large-scale expansion of web-based services in a hybrid cloud environment has created significant observability challenges for the IT operators and analysts charged with application performance monitoring and maintaining the security and operational efficiency of IT systems and user experience.
An 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 sources that were previously siloed 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 to quickly determine why a problem occurred and implement a solution if one is available to reduce the mean time to resolution (MTTR).
Incident management is a core function of the IT service desk in any IT organization. AIOps software tools effectively support the incident management process by automating responses to routine alerts, significantly reducing the time that IT operators spend doing mundane, low-value tasks. AIOps tools can also feed machine-enriched data directly into the incident management processes, acting as valuable sources of data and analysis that drive IT improvements for end users.
AIOps is best described as a set of technologies that make up a platform, rather than a single application. Today's available AIOps platforms certainly differ in their feature offerings. Still, the commonality they all share is that they 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:
One of the core capabilities of AIOps software is that it aggregates data from a variety of sources within DevOps infrastructure, including 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 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.
Rule and patterns
To accurately detect network events that warrant a response, artificial intelligence tools use rule application and pattern recognition algorithms. 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 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 capability
The defining feature of AIOps. When it comes to AIOps technology, artificial intelligence implementations are geared towards "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.
Reducing workload for IT operators is one of the main reasons that 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
AIOps software tools vary significantly, but they may follow the same basic workflows and possess the same core features to serve similar AIOps use cases. Successful AIOps software implementations can help enterprise IT organizations increase their oversight of hybrid cloud environments, detect and respond to network security events, provide remediation for those events more quickly and save time by automating routine tasks and processes.
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 it easy to aggregate data across the tech stack and down the telemetry pipeline. Sumo Logic is working toward a unified collection model that fits with 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 cyberattacks, 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.
Reduce downtime and move from reactive to proactive monitoring.