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Estimating lightpath Quality of Transmission (QoT) is crucial in network design and service provisioning. Recent studies have turned to Machine Learning (ML) techniques to improve the accuracy of QoT estimation. We distinguish two categories of solutions: the first category aims to build ML-based QoT estimation models that outperform the analytical model while the second category uses ML algorithms to reduce uncertainties on parameters provided as input to analytical model. In this overview, we describe the solutions in each category and discuss their practical feasibility and added benefit for operational networks.

We propose a Bayesian Optimization-based algorithm to assess the optical parameters that are taken as input by the QoT computation tool. The method reduces the error in computed OSNR down to 0.07dB.

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