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Edge computing, a key part of the 5G networks and beyond, promises to decentralize cloud applications while providing more bandwidth and reducing latencies. The promises are delivered by moving application-specific computations between the cloud, the data-producing devices, and the network infrastructure components at the edges of wireless and fixed networks. However, the current AI/ML methods assume computations are conducted in a powerful computational infrastructure, such as a homogeneous cloud with ample computing and data storage resources available. In this seminar, we discussed and developed presumptions for a comprehensive view of AI methods and capabilities in the context of edge computing, and provided a roadmap to bring together enablers and key aspects for edge computing and applied AI/ML fields.
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