AIOps, i.e., artificial intelligence for IT operations, has become the latest strategy du jour in the IT operations management space to help address and better manage the growing complexity and extreme scale of modern IT environments. AIOps enables some unique and new capabilities on this front, though it is quite a bit more complicated than the panacea that it is made out to be. However, the underlying AI and machine learning (ML) concepts do help complement, supplement and, in particular cases, even supplant more traditional approaches to handling typical IT Ops scenarios at scale.

An AIOps platform has to ingest and deal with multiple types of data to develop a comprehensive understanding of the state of the managed domain(s) and to better discern the push and pull of diverse trends in the environment, both overt and subtle, that may destabilize critical business outcomes. In this white paper, we will take a look at an AIOps approach to handling one of the fundamental data types: events.

Over the last few years, many IT monitoring solutions haven't kept pace with the advances of modern IT infrastructures. Many organizations still rely on disparate and siloed monitoring tools built on legacy framework that present a fragmented view of IT operations. It is increasingly clear that tools developed to keep a single system or a small cluster running are no longer sufficient in today's highly distributed, complex environment.

If you are in IT Ops or DevOps, hardly a day goes by without someone mentioning AIOps. There are a few who think AIOps can replace IT Ops tools today. Others debate this, saying that AIOps is still a nascent field, and it will take a few more years until we see a full-fledged AIOps platform for IT operations management. But there's always been a lot of confusion on how AIOps really works.