AIOps is among the esoteric disciplines information technology managers encounter these days. Many would argue that is the case because information technology operations these days are complex and decentralized.
To a large degree, that is because modern IT operations no longer are conducted “in house,” but also integrate cloud computing operations, virtual servers spinning up and down, containers sharing operating systems and providing microservices, mobile device and app support. New Internet of Things apps and devices, cloud storage, software as a service and platform as a service all contribute to complexity.
Among the issues is that AIOps--like artificial computing generally--is not a product. It is instead a capability that enhances other products and services. “AIOps isn’t a specific off-the-shelf product,” say Tony Branton and Ted Coombs, authors of a ServiceNow edition of “AIOps for Dummies.”
Specifically, AIOps is about applying machine learning to supervision processes. Right now, that mostly means AIOps “sits on top” of existing management tools. “Machine learning is a branch of AI that uses the ability of computers to learn by analyzing data and improving answers to questions posed to it autonomously,” the authors say.
Typically, that means applying supervised or unsupervised learning routines to security or asset management; root-cause analysis; change management; impact analysis, capacity management; performance and availability management.
