Machine learning with data synthesization

A method comprising:

• receiving, by one or more processors, data from a plurality of groups of data sources;
• accessing, by the one or more processors, a plurality of data synthetization machine learning models configured for generating synthetic data, respective ones of the data synthetization machine learning models corresponding to respective ones of the groups of data sources;
• generating first synthetic data by inputting, to a first one of the data synthetization machine learning models, first data received from a first data source corresponding to a first group of data sources for which the first data synthetization machine learning model is trained;
• generating second synthetic data by inputting, to a second one of the data synthetization machine learning models, second data received from a second data source corresponding to a second group of data sources for which the second data synthetization machine learning model is trained;
• determining a first graph using the first data received from the first data source and the first synthetic data generated by the first data synthetization machine learning model;
• determining a second graph using the second data received from the second data source and the second synthetic data generated by the second data synthetization machine learning model;
• and
• determining an allocation of resources based at least in part on comparing the first data and the first synthetic data with the second data and the second synthetic data, wherein the determining the allocation of resources comprises comparing the first graph with the second graph.

A non-transitory computer-readable medium maintaining instructions executable to configure one or more processors to perform operations comprising:

• receiving, by the one or more processors, data from a plurality of groups of data sources;
• accessing, by the one or more processors, a plurality of data synthetization machine learning models configured for generating synthetic data, respective ones of the data synthetization machine learning models corresponding to respective ones of the groups of data sources;
• generating first synthetic data by inputting, to a first one of the data synthetization machine learning models, first data received from a first data source corresponding to a first group of data sources for which the first data synthetization machine learning model is trained;
• generating second synthetic data by inputting, to a second one of the data synthetization machine learning models, second data received from a second data source corresponding to a second group of data sources for which the second data synthetization machine learning model is trained;
• determining a first graph using the first data received from the first data source and the first synthetic data generated by the first data synthetization machine learning model;
• determining a second graph using the second data received from the second data source and the second synthetic data generated by the second data synthetization machine learning model;
• and
• determining an allocation of resources based at least in part on comparing the first data and the first synthetic data with the second data and the second synthetic data, wherein the determining the allocation of resources comprises comparing the first graph with the second graph.