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AIOps applies automation rules and machine learning to simplify real-time work for organizations dealing with increasingly large amounts of data. Typically, AIOps solutions solve for noise reduction, root cause analysis, automation and auto-remediation, and analytics. For a deeper dive, click here.

An event in AIOps is a discrete, time-stamped occurrence or change in state that is detected within an IT infrastructure or application environment, typically containing contextual information about its source, type, and severity level. Events serve as fundamental data points that represent meaningful changes, incidents, or notifications requiring attention, ranging from system performance metrics and application errors to security alerts and infrastructure changes.

With 700+ native integrations, extensible APIs, and customizable workflows, PagerDuty can fit into and augment any team's toolkit, including automation tools.

In contrast to supervised machine learning, which is usually very precise when it is launched, but decays rapidly over time without significant maintenance, unsupervised models improve over time and don't require maintenance, resulting in more durable value. PagerDuty has built-in flexibility around how to set up unsupervised machine learning models so that you can define your rules at deployment and the model it trains itself thereafter.

Yes, you can enrich events and trigger self-healing actions based on event data across one or many services.

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PagerDuty follows our published guidelines for the safe and secure use of Generative AI. This is part of our broader Security and Privacy by Design discipline and includes protection of customer data in transit and at rest, and controls on who and how we process customer data. We will offer customer choice in the use of Generative AI features, including whether customer data is or may be used for training purposes. Currently no customer data is used for training purposes.