  The proliferation of emergent network applications (e.g., telesurgery,
metaverse) is increasing the difficulty of managing modern communication
networks. These applications entail stringent network requirements (e.g.,
ultra-low deterministic latency), which hinders network operators to manage
their resources efficiently. In this article, we introduce the network digital
twin (NDT), a renovated concept of classical network modeling tools whose goal
is to build accurate data-driven network models that can operate in real-time.
We describe the general architecture of the NDT and argue that modern machine
learning (ML) technologies enable building some of its core components. Then,
we present a case study that leverages a ML-based NDT for network performance
evaluation and apply it to routing optimization in a QoS-aware use case.
Lastly, we describe some key open challenges and research opportunities yet to
be explored to achieve effective deployment of NDTs in real-world networks.
