DevOps has quickly become a core part of how many organizations deliver IT, and in particular, how they deliver applications. But just as quickly as it has become popular, a whole series of XXXOps names have appeared. One of the latest is AIOps. So is it just yet another almost meaningless acronym?
Well as Betteridges Law of Headlines suggests, the answer is no.
When I first saw the term, I was presuming this would be about how to deploy AI based systems, and I wondered why on earth that would need a special name. But that's not what it is.
So what is AIOps?
AIOps is the use of artificial intelligence (AI) and machine learning (ML) techniques to allow us to analyze IT problems that occur so that we can respond to them fast enough to be useful.
The core problem is that we're now generating an enormous volume of metric and log data about every step of all our processes, and about the health of all our systems and applications yet so much of that data is never processed, or at least not processed fast enough to be useful.
Only machines can do this.
The term AIOps seems to have been coined by Will Cappelli (a former analyst with Gartner). In the end, humans won't be scouring logs and responding to what they find. Instead, they'll be teaching the machines what to look for, and how to correlate information from a variety of sources to find what is really going on.
Cappelli is now at Moogsoft and sums up AIOps quite distinctly:
AIOps is the application of artificial intelligence for IT operations. It is the future of ITOps, combining algorithmic and human intelligence to provide full visibility into the state and performance of the IT systems that businesses rely on.
People are already doing this but it's likely in the future that this will become a well-known job role. It will be important to guide the machine's learning to teach it to recognize the appropriate patterns.
If you are working in related IT roles, it might be time to start to add some data science, AI, and/or ML into your learning plans.