This tutorial identifies and discusses the main design choices and challenges arising in the application of machine learning (ML) to optical network failure management (ONFM), including quality of transmission estimation, failure detection, prediction, root-cause identification . This tutorial identifies and discusses the main design choices and challenges arising in the application of machine learning (ML) to optical network failure management (ONFM), including quality of transmission estimation, failure detection, prediction, root-cause identification . Abstract—Failure management plays a role of capital impor-tance in optical networks to avoid service disruptions and to satisfy customers' service level agreements. Machine Learning (ML) promises to revolutionize the (mostly manual and human-driven) approaches in which failure management in optical. However, such networks are susceptible to several failures (e., optical fiber cuts, malfunctioning optical devices), which might result in degradation in the network operation, massive data loss, and network disruption. This article explores comprehensive reliability engineering practices for 800G and 400G optical modules, from design principles to predictive maintenance strategies.
