Systems and methods for selecting a machine learning model and training images for the machine learning model

A device, comprising:

• one or more processors configured to:
• receive network data identifying at least one of a signal-to-interference-plus-noise ratio (SINR), a bit error rate (BER), a packet loss, or a frame loss associated with a radio access network (RAN);
• receive model data associated with a plurality of machine learning models;
• receive inference confidence scores, associated with the plurality of machine learning models, that are predicted using historical data associated with the plurality of machine learning models and using the network data to estimate results generated by the plurality of machine learning models;
• determine one or more machine learning models, of the plurality of machine learning models, to be considered for selection, the one or more machine learning models being determined based on values of the network data satisfying a first network threshold and a second network threshold for each of the one or more machine learning models;
• select a machine learning model of the one or more machine learning models based on comparing a previously selected machine learning model with the selected machine learning model;
• and
• cause the selected machine learning model to be implemented by a multi-access edge computing device associated with the RAN.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive network data identifying at least one of a signal-to-interference-plus-noise ratio (SINR), a bit error rate (BER), a packet loss, or a frame loss associated with a radio access network (RAN);
• receive model data associated with a plurality of machine learning models;
• receive inference confidence scores, associated with the plurality of machine learning models, that are predicted using historical data associated with the plurality of machine learning models and using the network data to estimate results generated by the plurality of machine learning models;
• determine one or more machine learning models, of the plurality of machine learning models, to be considered for selection, the one or more machine learning models being determined based on values of the network data satisfying a first network threshold and a second network threshold for each of the one or more machine learning models;
• select a machine learning model of the one or more machine learning models based on comparing a previously selected machine learning model with the selected machine learning model;
• cause the selected machine learning model to be implemented;
• receive image data identifying images to be processed by the selected machine learning model;
• receive an inference confidence score associated with a quality of the image data;
• process the network data, the image data, and the inference confidence score, with another model, to select images, from the image data, for training the selected machine learning model;
• and
• utilize the selected images to train the selected machine learning model.