  To meet the diverse demands for wireless communication, fifth-generation (5G)
networks and beyond (B5G) embrace the concept of network slicing by forging
virtual instances (slices) of its physical infrastructure. While network
slicing constitutes dynamic allocation of core network and radio access network
(RAN) resources, this article emphasizes RAN slicing (RAN-S) design. Forming
on-demand RAN-S that can be flexibly (re)-configured while ensuring slice
isolation is challenging. A variety of machine learning (ML) techniques have
been recently introduced for traffic forecasting and classification, resource
usage prediction, admission control, scheduling, and dynamic resource
allocation in RAN-S. Albeit these approaches grant opportunities towards
intelligent RAN-S design, they raise critical challenges that need to be
examined. This article underlines the opportunities and the challenges of
incorporating ML into RAN-S by reviewing the cutting-edge ML-based techniques
for RAN-S. It also draws few directions for future research towards intelligent
RAN-S (iRAN-S).
