  Lots of hopes have been placed on Machine Learning (ML) as a key enabler of
future wireless networks. By taking advantage of large volumes of data, ML is
expected to deal with the ever-increasing complexity of networking problems.
Unfortunately, current networks are not yet prepared to support the ensuing
requirements of ML-based applications in terms of data collection, processing,
and output distribution. This article points out the architectural requirements
that are needed to pervasively include ML as part of future wireless networks
operation. Specifically, we look into Wireless Local Area Networks (WLANs),
which, due to their nature can be found in multiple forms, ranging from
cloud-based to edge-computing-like deployments. In particular, we propose to
adopt the International Telecommunications Union (ITU) unified architecture for
5G and beyond. Based on ITU's architecture, we provide insights on the main
requirements and the major challenges of introducing ML to the multiple
modalities of WLANs. Finally, we showcase the superiority of the architecture
through an ML-enabled use case for future networks.
