Automation with AI

We discussed deploying AI applications and ML models at the edge in Chapter 5, which is becoming a common scenario because enterprises are adamant about reducing the time for decision-making and minimizing data movement. In Chapter 4, we touched upon using AI/ML applications to determine network traffic patterns and using automation to perform network maintenance and monitor network performance. This latter discussion, albeit brief, is more in keeping with the automation theme.

Using AI techniques to automate facets of the edge computing paradigm will allow for automation at scale. With so much data being generated by edge devices, enterprises are finding ways to not only infer and analyze that data but also create a corpus that can be used to learn from, build, and train new models. We now see the rise of such corpus models as Large Language Models (LLMs).

LLMs and generative AI

LLMs are massive amounts of data gathered from numerous existing sources that...