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AlOps space has rapidly evolved since Gartner defined this product space in 2016. It began an approach to applying emerging Machine Learning (ML) and statistical inferencing techniques to infrastructure datasets to gain better insights using Al techniques.

Universities and institutions for higher learning often have very multinational faculty, staff, and students with family, friends, and research collaborators from all over the world..

Traditionally, every enterprise office location had a border device (“Router” or “Gateway”);and that device defined the boundary as well as the perimeter for the  enterprise IT infrastructure. But not anymore! Instead, the definition of the boundary itself is becoming fuzzy and blurred.

Digital transformation, being widely embraced by enterprises today, has many economic benefits; however, it is also causing the enterprise’s IT operations to become increasingly chaotic. To reduce this chaos and create determinism in digital operations, AIOps (Artificial Intelligence for IT Operations) can play a very important role.

Digital transformation is really not an option, it is mandatory in the world we live in today – driven primarily by the way products and services are put together and then consumed by the end-users.

Since Gartner coined the buzzword “AIOps” a few years ago and predicted that the IT operations world would undergo a major shift from traditional IT management techniques, a hype grew around the term “AIOps”. Growingly, it feels like what “AIOps” is currently to the “IT Operations” world, is similar to what “SDN” used to be for the “Networking” world a few years back.

This series of blog posts are intended to capture some significant events and inventions that shaped evolution of networks as they exist today. As an academician, researcher, and inventor, I have been a part of this evolution directly and a witness to many turning points. I will attempt to provide some perspectives along the way. We will begin this series with “Network Intrusion Detection”.