A key enabler for integration of machine-learning models in network management is timely access to reliable data, in terms of features, which require pervasive measurement points throughout the network infrastructure. However, excessive measurements and monitoring is associated with network overhead. The demonstrator described in this paper shows key aspects of feature selection using a novel method based on unsupervised feature selection that provides a structured approach in incorporation of network-management domain knowledge in terms of policies. The demonstrator showcases the benefits of the approach in a 5G-mmWave network scenario where the model is trained to predict round-trip time as experienced by a user.