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 an offline testing of the latent model as compared to production copy; and 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 and cloning the latent model to replace the production copy template.