Dynatrace sponsored this podcast. The Kubernetes era has made scaled-out applications on multiple cloud environments a reality. But it has also introduced a tremendous amount of complexity into IT departments. My guest on this episode of The New Stack Makers podcast is Andreas Grabner from software intelligence platform Dynatrace, who recently noted that “in the enterprise Kubernetes environments I’ve seen, there are billions of interdependencies to account for.” Yes, billions. Grabner, who describes himself as a “DevOps Activist,” argues that AI technology can tame this otherwise overwhelming Kubernetes complexity. As he put it in a contributed post, “AI-powered observability provides enterprises with a host of new capabilities to better deploy and manage their Kubernetes environments.” During the podcast, we dig into how AI – and automation in general – is impacting observability in Kubernetes environments. To kick the show off, I asked Grabner to clarify what he means by “AI observability.”
Dynatrace sponsored this podcast.
The Kubernetes era has made scaled-out applications on multiple cloud environments a reality. But it has also introduced a tremendous amount of complexity into IT departments.
My guest on this episode of The New Stack Makers podcast is Andreas Grabner from software intelligence platform Dynatrace, who recently noted that “in the enterprise Kubernetes environments I’ve seen, there are billions of interdependencies to account for.” Yes, billions.
Grabner, who describes himself as a “DevOps Activist,” argues that AI technology can tame this otherwise overwhelming Kubernetes complexity. As he put it in a contributed post, “AI-powered observability provides enterprises with a host of new capabilities to better deploy and manage their Kubernetes environments.”
During the podcast, we dig into how AI – and automation in general – is impacting observability in Kubernetes environments. To kick the show off, I asked Grabner to clarify what he means by “AI observability.”