| CPC G06F 16/2365 (2019.01) | 20 Claims |
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1. A computer-implemented method comprising:
determining training data comprising:
a history of validation requests, wherein each historical validation request identifies one or more resources to be aggregated and validated; and
an indication of validation results corresponding to each historical validation request:
generating a trained machine learning model by training, using the training data, a machine learning model to perform validation of input aggregated resources;
tracking metrics by monitoring activity of serverless applications;
generating, based on determining that the metrics satisfy a threshold corresponding to a change in status of one or more of the serverless applications, a resource request;
aggregating, based on the resource request, data of one or more resources to generate an aggregated resource;
distributing, based on a validator identifier, the aggregated resource to one or more registered validators associated with the validator identifier;
validating the aggregated resource by:
providing, to the trained machine learning model, at least a portion of the aggregated resource; and
determining, based on output of the trained machine learning model, validation results; and
writing the validation results to a results database.
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