Reuse of machine learning models

A non-transitory computer-readable medium storing instructions which, when executed by a processing system including at least one processor, cause the processing system to perform operations, the operations comprising:

• detecting a plurality of parameters associated with a proposal for a proposed machine learning model to be built;
• identifying an existing machine learning model, wherein the existing machine learning model shares a similarity with the proposed machine learning model;
• evaluating a fitness of the existing machine learning model for reuse in building the proposed machine learning model;
• building a new machine learning model that is consistent with the proposal for the proposed machine learning model by reusing a portion of the existing machine learning model;
• and
• updating the new machine learning model subsequent to the new machine learning model being deployed in a deployment environment, wherein the updating is performed in response to a bias being detected in input features used to train the existing machine learning model, wherein the updating includes retraining the new machine learning model with new input features, and wherein the updating improves a quality of an output produced by the new machine learning model by mitigating the bias.

A device comprising:

• a processing system including at least one processor;
• and
• a non-transitory computer-readable medium storing instructions which, when executed by the processing system, cause the processing system to perform operations, the operations comprising:
• detecting a plurality of parameters associated with a proposal for a proposed machine learning model to be built;
• identifying an existing machine learning model, wherein the existing machine learning model shares a similarity with the proposed machine learning model;
• evaluating a fitness of the existing machine learning model for reuse in building the proposed machine learning model;
• building a new machine learning model that is consistent with the proposal for the proposed machine learning model by reusing a portion of the existing machine learning model;
• and
• updating the new machine learning model subsequent to the new machine learning model being deployed in a deployment environment, wherein the updating is performed in response to a bias being detected in input features used to train the existing machine learning model, wherein the updating includes retraining the new machine learning model with new input features, and wherein the updating improves a quality of an output produced by the new machine learning model by mitigating the bias.