For quite some time now, data scientists and machine and deep learning (ML/DL) experts are using different techniques to automate triaging and analysis of tickets in various hi-tech sectors.
It is interesting to see how the world of networking is evolving to support the demands of technologies such as IoT. We will eventually see many more capabilities being added to network solutions to drive the IoT wave. It’s clear that networking games are only the beginning.
IoT, as we all know has taken the center stage in the past few years. Literally from many of the day to day appliances to automotive to flights, all data that the IoT devices send are taking the user experience and the analytics to the next level.
Adoption of Kubernetes into data centers or cloud has been remarkable since it was released in 2014. As an orchestrator of lightweight application containers, Kubernetes has emerged to handle and schedule diverse IT workloads, from virtualized network functions to AI/ML and GPU hardware resources.
Industries had undergone three revolutions till. First being, mechanization and using indirect combustion for powering up mechanization through water and steam power (Industry 1.0). Second revolution brought complicated machinery for mass production and used electrical power (Industry 2.0).
With around 30 billion connected devices out there, the Internet of Things (IoT) is no longer just a buzzword. It’s very much a part of our reality. It has already started proving itself useful in the supply chain, manufacturing, automobiles, and predictive maintenance areas
Involvement of leading open infrastructure projects such as OpenStack and OSM as MANO suggest that such solutions are possible using open-source projects with the added benefit of controlling developing infrastructure costs.
Modern IoT solutions communicate constantly with the digital world, producing data and insights previously thought impossible. With 31 billion connected devices to be available by 2020, it’s safe to say that IoT is here to stay.
Simple explanations of Machine Learning and Artificial Intelligence, and how they’re related... but not at all the same thing.
Edge computing is the critical architecture for enabling most 5G use cases. However, requirements and expectations from edge node in terms of computing resources is in question, and with the growing number of devices and data it could become cumbersome.