Basically, Machine Learning by itself is dumb, whether its in cars or in ITOps. It needs context and intent to be beneficial. In IT Ops, that means that it requires business context to work properly.

Role-based access control (RBAC) has become one of the main methods for system access control within large enterprises, assigning access to users based on their role in the organization. Employees are allowed to access only those resources that are necessary to effectively perform their assigned job duties.

It’s time for change – my first insight from GartnerIO, 2018. During my flight to Vegas I remembered that this was probably my 15th visit to a conference in Vegas (but who can really count…after a few, it all starts to blur together). What I do remember though is that, in each of these conferences, there was one very shiny buzzword. The reason I remember this so well is because, while everyone agreed on what the buzzword was, there was no consensus on what that word meant!

Chronically understaffed and constantly stressed-out IT Ops and NOC teams are overwhelmed by today’s IT noise. Artificial Intelligence (AI) and Machine Learning (ML) can help these teams because ML (and AI) are exceptionally good at processing enormous volumes of very complex data in real-time, or near real-time, and surfacing actionable insights. But ML successes in IT Ops are still hit-or-miss.

You’ve just recovered from a critical application outage and your team is being asked to report on root cause and recommended remediation steps later this afternoon. Can you quickly analyze all the data, identify all the leading events, and discern which one was responsible for the cascading failure?