Patent ID: 11956129
Assignee: CIENA CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G  H | IPC G  H

Claim 0:
1. A system comprising:
a processing device, and
a memory device configured to store a computer program having instructions that, when executed, enable the processing device to
obtain network information regarding the condition of a network,
using the network information, perform a hybrid Machine Learning (ML) technique that includes training and inference of a plurality of ML models to calculate metrics of the network,
wherein a first ML model and a second ML model of the plurality of ML models are trained utilizing a same version of a training dataset of the obtained network information when a size of the training dataset is less than a threshold,
wherein the first ML model is used for inference before the size of the training dataset reaches the threshold,
wherein the second ML model is used for inference after the size of the training dataset reaches the threshold, and
wherein the first ML model is more accurate than the second ML module at least when the size of the training dataset is less than the threshold, and the second ML model is at least one of faster to train or generates an inference faster than the first ML module, and
subsequently select one of the plurality of ML models based on a combination of the metrics for generating an inference utilizing only real-time data and the selected one of the plurality of ML models, wherein the selected ML model is selected based on one or more of an historic accuracy score of each of the plurality of ML models calculated during training, an expected accuracy score of each of the plurality of ML models for later use during inference, a computational cost of each of the plurality of ML models during training, a training time associated with each of the plurality of ML models, and an estimated inference time associated with each of the plurality of ML models.