Using machine learning to estimate or forecast resource use with time-varying demand in gaming platforms

A method to use a trained machine learning model to allocate computational resources to use in order to evaluate a target percentage of reports submitted by users of a gaming platform in which a number of the reports submitted varies over time, the method comprising:

• providing, to the trained machine learning model, a first input comprising an indication of a time period during which the target percentage of a first number of reports is to be evaluated, wherein the first number reports are submitted by the users of the gaming platform and identify incidents in which content of the gaming platform violates a policy of use associated with the gaming platform;
• obtaining, from the trained machine learning model, one or more outputs that identify (i) a number of computational resources to evaluate the target percentage of the number of reports during the time period, and (ii) a level of confidence that the number of computational resources is sufficient to evaluate the target percentage of the number reports during the time period;
• and
• based on the one or more outputs of the trained machine learning model, allocating at least the number of computational resources to evaluate the target percentage of the number of reports, wherein the at least the number of computational resources is determined by the trained machine learning model that receives the number of reports as input.

A system to use a trained machine learning model to allocate computational resources to use in order to evaluate a target percentage of reports submitted by users of a platform in which a number of the reports submitted varies over time, the system comprising:

• a memory that stores instructions;
• and
• a processing device, coupled to the memory, operable to execute the instructions to:
• provide, to the trained machine learning model, a first input comprising an indication of a time period during which the target percentage of a first number of reports is to be evaluated, wherein the first number reports are submitted by the users of the platform and identify incidents in which content of the platform violates a policy of use associated with the platform;
• obtain, from the trained machine learning model, one or more outputs that identify (i) a number of computational resources to evaluate the target percentage of the number of reports during the time period, and (ii) a level of confidence that the number of computational resources is sufficient to evaluate the target percentage of the number reports during the time period;
• and
• based on the one or more outputs of the trained machine learning model, allocate at least the number of computational resources to evaluate the target percentage of the number of reports, wherein the at least the number of computational resources is determined by the trained machine learning model that receives the number of reports as input.

A system to use a machine learning model to allocate computational resources sufficient to evaluate a target percentage of reports submitted by users of a platform in which a number of the reports varies over time, the system comprising:

• a memory that stores instructions;
• and
• a processing device, coupled to the memory, operable to execute the instructions to:
• generate training data for the machine learning model, wherein to generate the training data, the processing device is operable to execute the instructions to:
• generate first training input, wherein the first training input comprises a first number of reports at a first point in time, and wherein the first number of reports are submitted by the users of the platform and identify incidents in which content of the platform violates a policy of use associated with the platform;
• generate second training input, wherein the second training input comprises a first number of computational resources active at the first point in time;
• and
• generate first target output for the first training input and the second training input, wherein the first target output identifies a number of computational resources sufficient to evaluate the target percentage of the first number of reports;
• provide the training data to train the machine learning model on (i) a set of training inputs comprising the first training input and the second training input, and (ii) a set of target outputs comprising the first target output;
• and
• allocate a particular number of computational resources to evaluate the target percentage of the number of reports, wherein the particular number of computational resources is determined by the trained machine learning model that receives the number of reports as input.