Introduction Edge AI is enabling new capabilities by embedding AI functionality directly into smartphones, IoT devices, industrial machines, robots, vehicles, and servers located where the data is generated and used. This allows immediate feedback based on real-time...
Introduction Today’s networks are more complex than ever and becoming more so every day. The factors driving this ever-increasing complexity include: the widespread use of cloud services and the real-time web, streaming and IoT applications they host; last-mile...
Introduction Many industry observers believe AI is having its “iPhone moment” with the rapid rise of emerging large language models (LLMs) and generative AI (Gen AI) applications such as OpenAI’s ChatGPT. A big part of what sets Gen AI applications...
Introduction AI applications, especially those harnessing large language models (LLMs) for deep learning, have an insatiable appetite for data. LLMs require vast datasets for training and inference, resulting in massive volumes of network traffic. To mitigate...
In a traditional cloud computing implementation, data generated at the edge of the network is sent to centralized cloud servers for processing. These servers can be located anywhere in the world, often in data centers far from the data source. This model works well...
