Operations | Monitoring | ITSM | DevOps | Cloud

AIOps myths and how to avoid them Gartner coined the term AIOps in 2016 to refer to the combining of “big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.” In the five years since, AIOps has grown leaps and bounds — last year, AIOps was at the peak of the Gartner hype cycle.

It’s harder to understand and operate production systems in 2021 than it was in 2001. Why is that? Shouldn’t we have gotten better at this in the past two decades? There are valid reasons why it’s harder: The architecture of our systems has gotten a lot more sophisticated and complex over the past 20 years. We’re not running monoliths on a few beefy servers these days.

As we kick off the new year and our release of Shipa 1.5.0, dabbling in the art of the possible, what if it was possible to provide your developers with a single line of configuration to get their ideas into production. Shipa is an application and policy abstraction layer which easily integrates with your DevOps toolchains.

The success of your enterprise’s digital transformation relies in no small part on your hybrid cloud infrastructure, which SearchCloud Computing defines as “a cloud computing environment that uses a mix of on-premises, private cloud and third-party, public cloud services with orchestration between these platforms.” Because this infrastructure is not a homogeneous environment, migration, management, and optimization can be an ongoing challenge.