  As network complexity escalates, there is an increasing need for more
sophisticated methods to manage and operate these networks, focusing on
enhancing efficiency, reliability, and security. A wide range of Artificial
Intelligence (AI)/Machine Learning (ML) models are being developed in response.
These models are pivotal in automating decision-making, conducting predictive
analyses, managing networks proactively, enhancing security, and optimizing
network performance. They are foundational in shaping the future of networks,
collectively forming what is known as Network Intelligence (NI). Prominent
Standard-Defining Organizations (SDOs) are integrating NI into future network
architectures, particularly emphasizing the closed-loop approach. However,
existing methods for seamlessly integrating NI into network architectures are
not yet fully effective. This paper introduces an in-depth architectural design
for a Network Intelligence Stratum (NI Stratum). This stratum is supported by a
novel end-to-end NI orchestrator that supports closed-loop NI operations across
various network domains. The primary goal of this design is to streamline the
deployment and coordination of NI throughout the entire network infrastructure,
tackling issues related to scalability, conflict resolution, and effective data
management. We detail exhaustive workflows for managing the NI lifecycle and
demonstrate a reference implementation of the NI Stratum, focusing on its
compatibility and integration with current network systems and open-source
platforms such as Kubernetes and Kubeflow, as well as on its validation on
real-world environments. The paper also outlines major challenges and open
issues in deploying and managing NI.
