1. A method comprising: training a machine learning algorithm to create a predictive model; in a set of distinct records in a database system, the database system comprising a first database cluster H and a second database cluster L, for each record of the set of distinct records, using the predictive model to calculate a probability of the record being accessed; for each record of the set of distinct records, if the probability of the record being accessed as calculated is greater than a threshold value, then placing the record in the first database cluster H; for each record of the set of distinct records, if the probability of the record being accessed as calculated is not greater than the threshold value, then placing the record in the second database cluster L; receiving a request from a requester for at least one record of the set of distinct records; and presenting the at least one record from the set of distinct records to the requester in response to the request; wherein the method is implemented via execution of computer instructions configured to run on one or more processing modules and configured to be stored on one or more non-transitory memory storage modules; and wherein training the machine learning algorithm comprises: for each record in the set of distinct records, inputting a training feature vector associated with the record into the machine learning algorithm, the training feature vector associated with the record comprising a list of characteristics of the record; for each record in the set of distinct records, inputting a cost vector associated with the record into the machine learning algorithm, the cost vector associated with the record configured to train the machine learning algorithm to reduce a probability of a false negative prediction for the record; and iteratively operating the machine learning algorithm on each record in the set of distinct records to create the predictive model.