AI Edge and AI Learning are two different concepts related to artificial intelligence, each with its own significance.
AI Learning is the process of training AI models, often performed in cloud or data center environments. This is done on "supercomputers" which falls outside of what we are offering. During this phase, AI models are exposed to large datasets to learn patterns, make predictions, or perform tasks.
Once AI models are trained, they can be deployed for inference (implementation) on edge devices to make predictions or decisions based on new data.
AI Edge refers to the deployment of AI models on local devices for real-time processing, rather than relying on cloud-based servers or data centers. Here the NVIDIA Jetson (Original and the newer ORIN) Series of Edge processors give optimum results.
Another edge hardware approach is to use an x86 based embedded PC and install an AI module such as the M.2 form factor Hailo-8L or Hailo-15 into the embedded system. There are a number of other M.2 based products that can also be considered such as the Intel Movidius etc.
The two concepts work together in AI systems, where models are trained centrally and then deployed to edge devices for local, efficient, and real-time inference.
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