Adaptive optimization of a content item using continuously trained machine learning models

A method comprising:

• receiving a plurality of requests for content items, wherein each of the plurality of requests is associated with a respective target comprising a respective set of characteristics;
• identifying a first subset of a plurality of trained machine learning models that satisfy a reliability criterion and a second subset of the plurality of trained machine learning models that fail to satisfy the reliability criterion, wherein each trained machine learning model of the plurality of trained machine learning models is associated with a respective content template of a plurality of content templates and is trained to output a probability that a target associated with an input set of characteristics would perform a target action responsive to being presented with a content item generated based on the respective content template associated with the respective trained machine learning model;
• for each request of the plurality of requests, assigning the request to a first group or a second group based on a ratio of a number of trained machine learning models in the first subset to a number of trained machine learning models in the second subset;
• and
• for each request in the first group, generating a content item based on a content template associated with one of the first subset of the plurality of trained machine learning models.

A non-transitory computer readable storage medium comprising instructions that, when executed by a processor, cause the processor to perform operations comprising:

• receiving a plurality of requests for content items, wherein each of the plurality of requests is associated with a respective target comprising a respective set of characteristics;
• identifying a first subset of a plurality of trained machine learning models that satisfy a reliability criterion and a second subset of the plurality of trained machine learning models that fail to satisfy the reliability criterion, wherein each trained machine learning model of the plurality of trained machine learning models is associated with a respective content template of a plurality of content templates and is trained to output a probability that a target associated with an input set of characteristics would perform a target action responsive to being presented with a content item generated based on the respective content template associated with the respective trained machine learning model;
• for each request of the plurality of requests, assigning the request to a first group or a second group based on a ratio of a number of trained machine learning models in the first subset to a number of trained machine learning models in the second subset;
• and
• for each request in the first group, generating a content item based on a content template associated with one of the first subset of the plurality of trained machine learning models.