Abstract: Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.

Abstract: The technologies described herein are generally directed to modeling radio wave propagation in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include based on a graphical representation of a layout of a geographic area, identifying, by equipment comprising a processor, a feature of the geographic area relevant to propagation of a signal propagated from a signal point on the layout, resulting in an identified feature. The method can further comprise based on the identified feature and the signal point, generating, by the equipment, a feature map for the geographic area by employing a neural network, wherein the feature map comprises a map depicting estimates of the propagation of the signal at locations within the geographic area.

Abstract: Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.

Abstract: The technologies described herein are generally directed to modeling radio wave propagation in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include based on a graphical representation of a layout of a geographic area, identifying, by equipment comprising a processor, a feature of the geographic area relevant to propagation of a signal propagated from a signal point on the layout, resulting in an identified feature. The method can further comprise based on the identified feature and the signal point, generating, by the equipment, a feature map for the geographic area by employing a neural network, wherein the feature map comprises a map depicting estimates of the propagation of the signal at locations within the geographic area.

Abstract: Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.

Abstract: The technologies described herein are generally directed to facilitating operation of a system for implementing fifth generation (5G) or other next generation networks. In accordance with one or more embodiments, systems described herein can include a processor, and a memory that can store executable instructions that can facilitate receiving, from a second device, information corresponding to iterations of ones of a sited group of antennas generated by the second device by selecting the iterations of ones of the sited group of antennas, with a selected iteration of the selected iterations including a subgroup of the sited group of antennas that is designated for a simulation of an operation of the subgroup. Further, the operations include simulating respective operations of the selected iterations, including propagation mapping with respect to respective wireless signals associated with the selected iterations, and estimating respective utilizations of the respective wireless signals.