  The future 6G network is envisioned to be AI-native, and as such, ML models
will be pervasive in support of optimizing performance, reducing energy
consumption, and in coping with increasing complexity and heterogeneity. A key
challenge is automating the process of finding optimal model architectures
satisfying stringent requirements stemming from varying tasks, dynamicity and
available resources in the infrastructure and deployment positions. In this
paper, we describe and review the state-of-the-art in Neural Architecture
Search and Transfer Learning and their applicability in networking. Further, we
identify open research challenges and set directions with a specific focus on
three main requirements with elements unique to the future network, namely
combining NAS and TL, multi-objective search, and tabular data. Finally, we
outline and discuss both near-term and long-term work ahead.
