One challenge in scaling edge is that it's nearly everywhere, compared to the more centralized cloud, according to Mishali
Another challenge is training edge AI models.
"The approach today for edge AI is to do the inference very close to the data source," Mishali said.
He added that for training, data needs to be captured and then sent to a central data center or to the cloud. This requires a lot of power, and a lot of GPUs, to do effective training.
"This can be a real challenge for scaling applications," Mishali continued, adding that enterprises face the difficult challenge of making sure that data flows smoothly among numerous endpoints and bandwidths.
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