Network professionals will need to level-up existing skills in network management and optimization so they can accomplish such tasks as using machine-learning algorithms to predict network congestion and to improve network performance. They will also need to develop new skills in data analysis and visualization, NLP, outlier analysis, anomaly detection, and optimization algorithms. “I am not suggesting they become an AI developer or a data scientist,” Masood said, “but deeper understanding of the underlying algorithms and statistical models used to build networking specific AI systems will definitely give them a competitive edge over their non-AI-literate counterparts.”
Normandin said that a new role, NetDevOps, will emerge to manage AI-orchestrated networks. “Successful NetDevOps initiatives will look like fully automated environments that can deploy changes across networks, ready to be consumed in a DevOps approach all along the [continuous integration/continuous delivery] pipeline,” he said.
Programmable, software-defined, and cloud-based network environments have made NetDevOps possible through infrastructure-as-code and automation. “Now, network operations teams have to make their Agile revolution and accept the risk of more frequent changes and more automation,” Normandin said. “As a consequence, they’ll need to refocus on the main outcome: monitoring and guaranteeing the digital experience delivered by the networks.”
(Jeff Vance is a Network World contributing writer and the founder of Startup50.com, a site that discovers, analyzes, and ranks tech startups. Follow him on Twitter, @JWVance, or connect with him on LinkedIn.)