An apparatus for optimizing dynamically the performance of an optical network (4, 5-1, 5-2), said apparatus comprising: (a) at least one learning engine (2; 2-CC, 2-NMS; 2-NCU) adapted to update a learning model in response to network metrics (8-CC, 8-NMS, 8-NCU) of said optical network (4, 5-1, 5-2) collected during operation of said optical network (4, 5-1, 5-2), wherein the updated learning model is used to generate channel rank information for network channels; and (b) a recommendation engine (3; 3-CC) adapted to change a network channel throughput, a signal path and/or a spectral location of at least one network channel based on the channel rank information generated by the learning model of said learning engine (2; 2-CC, 2-NMS; 2-NCU); (c) wherein the learning model of said at least one learning engine (2; 2-CC, 2-NMS; 2-NCU) comprises an artificial neural network, ANN, trained in a training phase with training data; and adapted such that (d) the network metrics (8-CC; 8-NMS; 8-NCU) are monitored, collected and logged in real time during operation of said optical network (4, 5-1, 5-2) and comprise single or multi-layer metrics including - configuration metrics including spectral efficiency, amplifier types, fiber types, accumulated dispersion information and grid information, - operational metrics including at least shelf temperature and laser temperature - device metrics of network devices including at least laser wavelength, and - system metrics including bit error rate, optical signal-to-noise ratios, Q-factor, non-linear noise and transceiver power; and (e) wherein said recommendation engine (3; 3-CC) is adapted to determine for each network channel a number of channels copropagating on the same optical span along a signal path within said optical network (4, 5-1, 5-2) and to calculate a channel density of network channels per optical span of the signal path, and (f) wherein said recommendation engine (3; 3-CC) is adapted to optimize a network channel throughput of at least one network channel whose channel rank information indicates a highest channel rank and/or having the lowest determined number of copropagating network channels and/or whose signal path comprises the lowest calculated channel density. (a) at least one learning engine (2; 2-CC, 2-NMS; 2-NCU) adapted to update a learning model in response to network metrics (8-CC, 8-NMS, 8-NCU) of said optical network (4, 5-1, 5-2) collected during operation of said optical network (4, 5-1, 5-2), wherein the updated learning model is used to generate channel rank information for network channels; and (b) a recommendation engine (3; 3-CC) adapted to change a network channel throughput, a signal path and/or a spectral location of at least one network channel based on the channel rank information generated by the learning model of said learning engine (2; 2-CC, 2-NMS; 2-NCU); (c) wherein the learning model of said at least one learning engine (2; 2-CC, 2-NMS; 2-NCU) comprises an artificial neural network, ANN, trained in a training phase with training data; and adapted such that (d) the network metrics (8-CC; 8-NMS; 8-NCU) are monitored, collected and logged in real time during operation of said optical network (4, 5-1, 5-2) and comprise single or multi-layer metrics including - configuration metrics including spectral efficiency, amplifier types, fiber types, accumulated dispersion information and grid information, - operational metrics including at least shelf temperature and laser temperature - device metrics of network devices including at least laser wavelength, and - system metrics including bit error rate, optical signal-to-noise ratios, Q-factor, non-linear noise and transceiver power; and - configuration metrics including spectral efficiency, amplifier types, fiber types, accumulated dispersion information and grid information, - operational metrics including at least shelf temperature and laser temperature - device metrics of network devices including at least laser wavelength, and - system metrics including bit error rate, optical signal-to-noise ratios, Q-factor, non-linear noise and transceiver power; and (e) wherein said recommendation engine (3; 3-CC) is adapted to determine for each network channel a number of channels copropagating on the same optical span along a signal path within said optical network (4, 5-1, 5-2) and to calculate a channel density of network channels per optical span of the signal path, and (f) wherein said recommendation engine (3; 3-CC) is adapted to optimize a network channel throughput of at least one network channel whose channel rank information indicates a highest channel rank and/or having the lowest determined number of copropagating network channels and/or whose signal path comprises the lowest calculated channel density.