Edge AI: Deploying AI/ML on Devices
Challenges in deploying Edge AI applications
Introduction
AI/ML use-cases are pervasive. The enterprise use-cases can be broadly categorized based on the three core technical capabilities enabling them: Predictive Analytics, Computer Vision (CV) and Natural Language Processing (NLP). The Enterprise AI story has so far been focused on the Cloud. The general perception is that it takes a large amount of data and powerful machines, e.g., Graphical Processing Units (GPUs), to run AI applications.
Edge AI, also known as TinyML, aims to bring all the goodness of AI to the device. The idea is to bring the processing as close as possible to the devices generating the data.
In its simplest form, the device is able to process the data locally and instantaneously, without any dependency on the Cloud. Edge AI enables Visual, Location and Analytical solutions at the edge for diverse industries, such as Healthcare, Automotive, Manufacturing, Retail and Energy. According to a report by Market and Markets, “the global Edge AI software market size is expected to grow to USD 1,835 million by 2026”. Similarly, a report by 360 Research Reports estimates that “the global Edge…
