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Edge intelligence leverages computing resources on network edge to provideartificial intelligence (AI) services close to network users. As it enablesfast inference and distributed learning, edge intelligence is envisioned to bean important component of 6G networks. In this article, we investigate AIservice provisioning for supporting edge intelligence. First, we present thefeatures and requirements of AI services. Then, we introduce AI service datamanagement, and customize network slicing for AI services. Specifically, wepropose a novel resource pooling method to jointly manage service data andnetwork resources for AI services. A trace-driven case study demonstrates theeffectiveness of the proposed resource pooling method. Through this study, weillustrate the necessity, challenge, and potential of AI service provisioningon network edge.

Slicing-Based AI Service Provisioning on Network Edge