Blockchain-based model governance and auditable monitoring of machine learning models

A system comprising:

• at least one data processor;
• and
• at least one memory storing instructions, which when executed by the at least one processor result in operations comprising:
• determining, by a trained machine learning model, a score, wherein the trained machine learning model determines the score based at least on one or more latent features;
• monitoring the determining of the score by the trained machine learning model, wherein the monitoring includes:
• determining one or more production statistics associated with the one or more latent features;
• and
• accessing one or more reference assets persisted on a model governance blockchain, wherein the one or more reference assets were persisted during training of the trained machine learning model, and wherein the one or more reference assets include:
• one or more reference statistics associated with the one or more latent features;
• and a threshold indicating a deviation between the one or more production statistics and the one or more reference statistics;
• and
• generating an alert based on the one or more production statistics associated with the one or more latent features meeting the threshold.

A non-transitory computer-readable medium storing instructions, which when executed by at least one data processor, result in operations comprising:

• determining, by a trained machine learning model, a score, wherein the trained machine learning model determines the score based at least on one or more latent features;
• monitoring the determining of the score by the trained machine learning model, wherein the monitoring includes:
• determining one or more production statistics associated with the one or more latent features;
• and
• accessing one or more reference assets persisted on a model governance blockchain, wherein the one or more reference assets were persisted during training of the trained machine learning model, and wherein the one or more reference assets include:
• one or more reference statistics associated with the one or more latent features;
• and a threshold indicating a deviation between the one or more production statistics and the one or more reference statistics;
• and
• generating an alert based on the one or more production statistics associated with the one or more latent features meeting the threshold.