1. A computer-implemented method of processing tree models corresponding to user data of plural users, the method comprising the steps of: deploying a first architecture deployment instance, the first architecture deployment instance storing a respective tree model processing budget for each one of plural users against which tree model processing costs for the corresponding user are applied, providing a user interface process with which each user uploads its user data, providing a data analysis process to analyze each user's data and convert it into a corresponding dataset, and providing a model builder process to construct a corresponding decision tree model based on the dataset of each user, the first architecture deployment instance employing first computing resources; wherein providing the model builder process includes for a selected dataset, (a) distributing plural partitions of the selected dataset from a master process to plural respective worker processes; (b) until a predetermined tree model building criterion is met by a finished first tree model, distributing a first tree model of the selected dataset from the master process to the plural worker processes; (c) processing the respective partition of the selected dataset at each worker process with the first tree model to obtain a local tree model result; and (d) updating the first tree model at the master process according to one or more of the local tree model results and returning to step (b), in an iterative fashion, wherein updating the first tree model comprises growing an additional layer of the tree model in each iteration; and wherein updating the first tree model at the master process according to one or more of the local tree model results includes, at each worker process, compressing its local results into a series of histograms, one histogram for each input variable of the dataset, and transmitting the histograms to the master process.