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The combination of data-hungry services and high-throughput access technologies creates unprecedented capacity requirements in today's networks. Optical networks are the backbone of the telecom infrastructure, and optical network operators are challenged to increase capacity while keeping expenses under control. Expenses can be lowered if optical networks are operated more efficiently. In current deployments, optical transponders are configured with significant safety margins that guarantee very high availability, but are often overly pessimistic. Low-margin design refers to an optical-network design aimed at reducing safety margins and operating closer to channel capacity, while still guaranteeing high availability requirements. We investigate different approaches to low-margin design and quantify the savings achieveable with low-margin design. We first focus on quantifying the margin decrement that can be achieved using advanced modulation and coding schemes (ie, Probabilistic Constellation Shaping), using intelligent resource-allocation strategies (ie, nature-inspired metaheuristics) and precise Quality of Transmission modelling (ie, considering actual value of nonlinear interference rather than the worst-case one). Then, we focus on reducing the design margins used to account for imprecise knowledge of physical-layer parameters (eg, connector losses and optical amplifier gain profiles). We propose a design procedure based on Machine Learning (ML) that allows to gradually decrease these margins in presence of multiple physical-layer uncertainties by leveraging monitoring data. To this end, we introduce of a probabilistic ML …