Any developer, SRE or DevOps engineer responsible for an application with users has felt the pain of responding to a high priority incident. Read about 3 ways that ML can be a game changer in the incident management lifecycle.
Any developer, SRE or DevOps engineer responsible for an application with users has felt the pain of responding to a high priority incident. There’s the immediate stress of mitigating the issue as quickly as possible, often at odd hours and under severe time pressure. There’s the bigger challenge of identifying root cause so a durable fix can be put in place. There’s the aftermath of postmortems, reviews of your monitoring and observability solutions, and inevitable updates to alert rules. And there’s the typical frustration of wondering what could have been done to avoid the problem in the first place.
In a modern cloud native environment, the complexity of distributed applications and the pace of change make all of this ever harder. Fortunately, AI and ML technologies can help with these human-driven processes. Here are three specific ways: