Adaptive training of data driven analysis to characterize faults in physical systems

A method, comprising:

• constructing an affinity linked machine learning training dataset comprising a plurality of historical queries and historical commands test sampled from one or more production logs of a deployed diagnostic system;
• configuring one or more training data sourcing parameters to source the affinity linked machine learning training dataset from one or more remote sources of indication and action data;
• transmitting the one or more training data sourcing parameters to one or more remote sources of machine learning training data and collecting the affinity linked machine learning training dataset;
• calculating one or more validation metrics of the affinity linked machine learning training dataset, wherein calculating the one or more validation metrics includes calculating one or more of a coverage metric value and a diversity metric value of the affinity linked machine learning training dataset;
• identifying whether to train at least one machine learning classifier of the artificially intelligent diagnostic system based on one or more of the coverage metric value and the diversity metric value of the affinity linked machine learning training dataset;
• using the affinity linked machine learning training dataset to train the at least one machine learning classifier if the coverage metric value satisfies a minimum coverage metric threshold;
• and
• responsive to training the at least one machine learning classifier, deploying the at least one machine learning classifier into an online implementation of an artificially intelligent diagnostic system.

A method of creating affinity linked training data, the method comprising:

• capturing data comprising one or more data or image files to provide a delimited dataset;
• extracting one or more contextual tags from the one or more data or image files of the delimited dataset;
• indexing the one or more data or image files of the delimited dataset by the one or more contextual tags;
• partitioning the one or more data or image files of the delimited dataset into ingress data and egress data;
• performing action recognition processing on the egress data;
• performing indication recognition processing on the ingress data;
• generating a first data structure and second data structure of interleaved data;
• and
• unifying the first data structure and the second data structure into a training set for one or more processing models.