AI/ML-as-a-Service for optical network automation: use cases and challenges [Invited]

In recent years, artificial intelligence/machine learning (AI/ML) has played a significant role in automating optical networks. Despite this, the methods for creating, deploying, and monitoring AI/ML models still rely heavily on human intervention and trial-and-error. AI/ML-as-a-Service aims at automating the processes associated with AI/ML models, reducing the need for human intervention and thus facilitating the widespread adoption of AI/ML models. In this paper, we introduce the concept of AI/ML-as-a-Service in the context of optical network automation and propose an architecture for realizing this concept. We provide details of a reference implementation that focuses on the model creation stage. The reference implementation is tested using two use cases related to the quality-of-transmission (QoT) estimation of optical channels. We demonstrate that models created through AI/ML-as-a-Service are able to achieve similar performance as manually tuned models while drastically reducing the need for human involvement. Finally, we discuss future challenges and opportunities for applying AI/ML-as-a-Service in optical network automation.