Toward Deployment of ML in Optical Networks, Transfer Learning, Monitoring and Modelling

Abstract

We present a novel approach for Quality of Transmission estimation using hybrid modelling and transfer-learning. Our method reduces the training data requirement by 80% while obtaining an MSE of 0.27dB. The approach facilitates a streamlined ML life-cycle for data collection, training and deployment.

Toward Deployments of ML Applications in Optical Networks

“…The work Fig. 1: Hybrid model life-cycle management for machine learning algorithms in optical networks extended our work [12], by adding literature review of the management platform for machine learning model deployment and extra information about the experiments. Synthetic data gathered through coarse analytical modelling is used to obtain a QoT-prediction model with acceptable precision in the absence of practical network data.…”