Vehicle resiliency, driving feedback and risk assessment using machine learning-based vehicle wear scoring

A machine learning training and operation server comprising:

• one or more processors;
• and a memory storing instructions that, when executed by the one or more processors, cause the server to:
• retrieve a machine learning model;
• wherein the machine learning model is trained until a loss score meets a predetermined criteria by at least:
• inputting a training input and corresponding label;
• analyzing the training input to generate a training prediction;
• generating the loss score by comparing the training prediction to the label using a loss function;
• and
• modifying at least one of one or more weights and one or more biases of the machine learning model based at least in part upon the loss score;
• instantiate the trained machine learning model;
• receive an operational input;
• and
• analyze the operational input using the trained machine learning model to obtain a prediction
• wherein:
• the operational input includes a set of component wear scores of a vehicle, and
• the prediction includes an aggregated wear score of the vehicle generated by analyzing the set of component wear scores.

A computer-implemented method, the method including:

• retrieving, via one or more processors, a machine learning model;
• wherein the machine learning model is trained until a loss score meets a predetermined criteria by at least:
• inputting to the machine learning model a training input and corresponding label;
• analyzing the training input using the machine learning model to generate a training prediction;
• generating the loss score by comparing the training prediction to the label using a loss function;
• and
• modifying at least one of one or more weights and one or more biases of the machine learning model based at least in part upon the loss score;
• instantiating the trained machine learning model;
• receiving an operational input;
• and
• analyzing the operational input using the trained machine learning model to obtain a prediction;
• wherein:
• the operational input includes a set of component wear scores of a vehicle, and
• the prediction includes an aggregated wear score of the vehicle generated by analyzing the set of component wear scores.