  Edge intelligence leverages computing resources on network edge to provide
artificial intelligence (AI) services close to network users. As it enables
fast inference and distributed learning, edge intelligence is envisioned to be
an important component of 6G networks. In this article, we investigate AI
service provisioning for supporting edge intelligence. First, we present the
features and requirements of AI services. Then, we introduce AI service data
management, and customize network slicing for AI services. Specifically, we
propose a novel resource pooling method to jointly manage service data and
network resources for AI services. A trace-driven case study demonstrates the
effectiveness of the proposed resource pooling method. Through this study, we
illustrate the necessity, challenge, and potential of AI service provisioning
on network edge.
