1. A computer-implemented method, comprising: receiving a training configuration based on modifying a production copy template of a production machine learning model for an application service; scheduling a recurring training session based on the training configuration to produce a latent model; tracking one or more differences in training configurations of the latent model as compared to the production copy template; computing an evaluative metric of the latent model by performing testing of the latent model as compared to the production copy template; generating a machine learner interface to access a model tracker database that indexes the evaluative metric and the tracked differences associated with the latent model, wherein the machine learner interface provides an interface element to trigger launching the latent model into production; receiving a search query targeting a particular dataset or a particular data feature; and in response to receiving the search query, presenting one or more target machine learning models and evaluation statistics of the target machine learning models by querying the model tracker database to identify the target machine learning models based on the target machine learning models utilizing the particular dataset or the particular data feature.