A computer-implemented method to train a machine learning model to estimate for a time period a number of computer resources sufficient to evaluate a target percentage of reports submitted by users of a gaming platform where a number of the reports vary over time, the method comprising: generating training data for the machine learning model at fixed time intervals, defined by a plurality of points in time during the time period, which are identified by time stamps, and wherein generating the training data comprises: generating first training input, wherein the first training input comprises a first number of reports at a first point in time of the plurality of points in time, and wherein the reports are submitted by the users of the gaming platform and identify incidents where content of the gaming platform violates a policy of use associated with the gaming platform; generating second training input, wherein the second training input comprises a number of computer resources active at the first point in time; generating third training input, wherein the third training input comprises a percentage of the first number of reports that are being evaluated at the first point in time by the number of computer resources active at the first point in time, and generating fourth training input, wherein the fourth training input comprises an indication of a rate of change between the first number of reports at the first point in time and another number of reports at a previous point in time, and generating first target output for the first training input, the second training input, the third training input and the fourth training input, wherein the first target output identifies a number of computer resources sufficient to evaluate the target percentage of the first number of reports; and providing the training data to train the machine learning model on: a set of training inputs comprising the first training input, the second training input, the third training input and the fourth training input, and a set of target outputs comprising the first target output. generating training data for the machine learning model at fixed time intervals, defined by a plurality of points in time during the time period, which are identified by time stamps, and wherein generating the training data comprises: generating first training input, wherein the first training input comprises a first number of reports at a first point in time of the plurality of points in time, and wherein the reports are submitted by the users of the gaming platform and identify incidents where content of the gaming platform violates a policy of use associated with the gaming platform; generating second training input, wherein the second training input comprises a number of computer resources active at the first point in time; generating third training input, wherein the third training input comprises a percentage of the first number of reports that are being evaluated at the first point in time by the number of computer resources active at the first point in time, and generating fourth training input, wherein the fourth training input comprises an indication of a rate of change between the first number of reports at the first point in time and another number of reports at a previous point in time, and generating first target output for the first training input, the second training input, the third training input and the fourth training input, wherein the first target output identifies a number of computer resources sufficient to evaluate the target percentage of the first number of reports; and generating first training input, wherein the first training input comprises a first number of reports at a first point in time of the plurality of points in time, and wherein the reports are submitted by the users of the gaming platform and identify incidents where content of the gaming platform violates a policy of use associated with the gaming platform; generating second training input, wherein the second training input comprises a number of computer resources active at the first point in time; generating third training input, wherein the third training input comprises a percentage of the first number of reports that are being evaluated at the first point in time by the number of computer resources active at the first point in time, and generating fourth training input, wherein the fourth training input comprises an indication of a rate of change between the first number of reports at the first point in time and another number of reports at a previous point in time, and generating first target output for the first training input, the second training input, the third training input and the fourth training input, wherein the first target output identifies a number of computer resources sufficient to evaluate the target percentage of the first number of reports; and providing the training data to train the machine learning model on: a set of training inputs comprising the first training input, the second training input, the third training input and the fourth training input, and a set of target outputs comprising the first target output. a set of training inputs comprising the first training input, the second training input, the third training input and the fourth training input, and a set of target outputs comprising the first target output.