Today's resource managers and container orchestrators allow us to describe and deploy workloads in a consistent and repeatable fashion. So why are our workloads growing ever more complex?

There is a lot of industry buzz around how AIOps will affect change within IT Operations (ITOps). According to Gartner, Inc., the term “AIOps” describes platforms that combine big data and machine learning to support ITOps. This means that the problems being solved aren’t novel, the approach is. In ITOps or any other business unit, there are two primary constraints: time and money.

We’re proud to launch BigPanda’s Future of Monitoring, IT Operations and AIOps survey – a comprehensive, in-depth look at the current state of monitoring and IT Operations, and where the industry is headed.

If you’re part of an IT Ops or NOC team, or if you manage one, you know that overwhelming IT noise is your #1 enemy. Not only does it flood your teams with false-positives, but it also buries critical root-cause events and it makes it hard to proactively detect expensive P1 and P0 outages.

By augmenting operations teams, AIOps enables organizations to preemptively ensure that applications, architectures and infrastructures are ready for rapid digital transformation.

Built-in AI/ML—such as in AppDynamics APM—delivers value by activating the cognitive engine of AIOps to address anomalies.