AppDynamics surveyed 6,000 global IT leaders about application performance monitoring and AIOps. Read on to discover the trends shaping the space.

In a world where everything comes down to moments of truth, teams must respond to issues and opportunities in seconds. Rising customer expectations demand real-time response, and effective DevOps and ITOps shouldn’t just be tied to laptops and desks.

When we introduced ‘remote actions’ in 2012, i.e. the execution of IT automation tasks from your smartphone, we aimed at empowering the mobile (IT) workforce of the future. We aimed at relieving IT people from being bound to their desks, notebooks and PCs.

We caught up with IT leaders from today's most innovative brands to get their thoughts on how AI will transform IT operations. See what they have to say.

This blog post is a companion piece for my talk at https://devopsdaysindia.org. I will discuss the motivations, architecture, and the future of logging in Grafana! Let’s get right down to it. You can see the slides for the talk here.

Before I hop right in, it’s important to understand a bit about diabetes. Diabetes is what happens when your body cannot produce (type 1) or respond (type 2) to insulin effectively. The impact on the body is frequently quite severe — people who have difficulty controlling their blood sugar levels run the risk of losing feeling in their fingers and/or toes or even going into a coma if their blood sugar is either too high or too low.

2018 was an interesting year for Node.js frameworks and open source software in general. Developer communities discussed the role of corporate sponsorship and how to maintain a project used by millions but not supported financially.

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.