MR2 or MR3) and then sit on it for a long time, which means your solution stops evolving to solve business objectives with data. For stability, you’ll need to wait for a reliable maintenance release (i.e.
This pace of iterative change has proven very difficult to deliver with traditional enterprise software packages without bugs, migration issues, and support handholding.
But, it’s hard to get to that truth, or to tease apart the realities of general AI vs narrow AI.
Nonetheless, not every problem needs “data-driven AI,” since many networking challenges are well-solved by rule-driven, model-driven, or expert-driven approaches (more on this in a future post). Networking complexity keeps growing while businesses build deeper network integrations, so ML/AI becomes necessary to make networks more autonomous.
ML/AI is definitely here to stay, so we need to understand it. The only topic with more industry hysteria might be 5G. Get this: in the 2019 hype cycle, Gartner lists 29 emerging technologies, and at least 16 of them are related to data science, machine learning (ML), and artificial intelligence (AI)! Over half! That’s quite a dose of hype for one chart. Every year, Gartner publishes a hype cycle chart for emerging technologies.