Abstract: A device may receive real time telecommunications data associated with a telecommunications network, and may select a first machine learning model from a first plurality of machine learning models based on the real time telecommunications data. The device may select a second machine learning model from a second plurality of machine learning models based on the real time telecommunications data. The device may process, in a first stage, the real time telecommunications data, with the first machine learning model, to determine a probability score indicating whether a customer technical problem telephone call will occur. The device may process, in a second stage and based on the probability score satisfying a threshold, the real time telecommunications data, with the second machine learning model, to determine a category associated with the customer technical problem telephone call, and may perform one or more actions based on the category.

Abstract: In some implementations, a device may receive, from a monitoring device, telecommunications data associated with a telecommunications network. The device may train a plurality of machine learning models with the telecommunications data to generate a plurality of trained machine learning models. The device may generate accuracy scores for the plurality of trained machine learning models based on training the plurality of machine learning models. The device may select an optimum machine learning model based on several indicators, such as the accuracy, precision, and/or the like. The device may provide the optimum machine learning model to the monitoring device associated with the telecommunications network.

Abstract: In some implementations, a device may receive, from a monitoring device, telecommunications data associated with a telecommunications network. The device may train a plurality of machine learning models with the telecommunications data to generate a plurality of trained machine learning models. The device may generate accuracy scores for the plurality of trained machine learning models based on training the plurality of machine learning models. The device may select an optimum machine learning model based on several indicators, such as the accuracy, precision, and/or the like. The device may provide the optimum machine learning model to the monitoring device associated with the telecommunications network.

Abstract: A device may receive real time telecommunications data associated with a telecommunications network, and may select a first machine learning model from a first plurality of machine learning models based on the real time telecommunications data. The device may select a second machine learning model from a second plurality of machine learning models based on the real time telecommunications data. The device may process, in a first stage, the real time telecommunications data, with the first machine learning model, to determine a probability score indicating whether a customer technical problem telephone call will occur. The device may process, in a second stage and based on the probability score satisfying a threshold, the real time telecommunications data, with the second machine learning model, to determine a category associated with the customer technical problem telephone call, and may perform one or more actions based on the category.

Abstract: According to an example, a network deployment system facilitates deploying of cellular network infrastructure, including cellular network equipment for cellular sites and connection of the cellular sites to a backhaul network, or deploying of fiber optic network infrastructure. Workflows are generated and executed for deploying the network infrastructure.

Abstract: Implementations are directed to receiving behavior data and line parameter data from a plurality of user devices in real-time, each user device being associated with a respective communication line, processing the behavior data and line parameter data through a predictive model, the predictive model having been trained using a set of training data including previously received behavior data and previously received line parameter data, providing at least one risk score for each communication line based on the processing, each risk score representing a likelihood that a trouble ticket for the respective communication line would be opened within a determined temporal period, and selectively performing one or more recovery actions for a communication line based on a respective risk score, the one or more recovery actions being performed to inhibit opening of at least one trouble ticket.

Abstract: According to an example, a network deployment system facilitates deploying of cellular network infrastructure, including cellular network equipment for cellular sites and connection of the cellular sites to a backhaul network, or deploying of fiber optic network infrastructure. Workflows are generated and executed for deploying the network infrastructure.