Abstract: A device may receive network data, business data, and user configuration data associated with an entity that is a candidate for a private network and may process the business data and the user configuration data, with a classification machine learning model, to determine a network hardware equipment prediction. The device may process the network data and the business data, with a first linear regression machine learning model, to determine a business output prediction and may utilize a second linear regression machine learning model to determine a data consumption prediction based on the network hardware equipment prediction. The device may process the network hardware equipment prediction, the business output prediction, and the data consumption prediction, with a machine learning model, to determine a financial profitability prediction for the private network and may perform one or more actions based on the financial profitability prediction.

Abstract: A computing architecture providing for rapid analysis and control of an environment via edge computing nodes is disclosed. Input data streams may be captured via one or more data stream independent CPU threads and prepared for processing by one or more machine learning models. The machine learning models may be trained according to different use cases to facilitate a multi-faceted and comprehensive analysis of the input data. The evaluation of the input data against the machine learning models may be facilitated via independent GPU threads (e.g., one thread per model or use case) and the outputs of the models may be evaluated using control logic to produce a set of outcomes and control data. The control data may be utilized to generate one or more command messages that may provide feedback to a remote device or user regarding a state of a monitored environment or other observed condition.

Abstract: A device may receive network data, business data, and user configuration data associated with an entity that is a candidate for a private network and may process the business data and the user configuration data, with a classification machine learning model, to determine a network hardware equipment prediction. The device may process the network data and the business data, with a first linear regression machine learning model, to determine a business output prediction and may utilize a second linear regression machine learning model to determine a data consumption prediction based on the network hardware equipment prediction. The device may process the network hardware equipment prediction, the business output prediction, and the data consumption prediction, with a machine learning model, to determine a financial profitability prediction for the private network and may perform one or more actions based on the financial profitability prediction.