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 …

Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future
wireless networks. By taking advantage of the large volumes of data generated by networks,
ML is expected to deal with the ever-increasing complexity of networking problems.
Unfortunately, current networking systems are not yet prepared for supporting the ensuing
requirements of ML-based applications, especially for enabling procedures related to data
collection, processing, and output distribution. This article points out the architectural …