Machine learned resource allocation models for centralized database predictions

A non-transitory computer-readable storage medium storing executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• accessing, by a central database system for each of a set of historical entities, historical data describing 1) a historical time period, 2) historical actions requested by the historical entity during the historical time period, and 3) historical resources required by the central database system to perform the requested historical actions during the historical time period;
• generating, by the central database system, a training set of data based on the accessed historical data;
• training, by the central database system, a machine-learned model using the training set of data, the machine-learned model configured to predict, for an entity and a time period, resources required to perform actions requested by the entity during the time period;
• applying, by the central database system, the machine-learned model to a target entity and a target time period to predict a set of target resources that will be required for the target entity and the target time period;
• and
• modifying, by the central database system, a configuration of the central database system such that the predicted set of target resources are available to the target entity in advance of the target time period.

A central database system comprising one or more hardware processors and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations comprising:

• accessing, by the central database system for each of a set of historical entities, historical data describing 1) a historical time period, 2) historical actions requested by the historical entity during the historical time period, and 3) historical resources required by the central database system to perform the requested historical actions during the historical time period;
• generating, by the central database system, a training set of data based on the accessed historical data;
• training, by the central database system, a machine-learned model using the training set of data, the machine-learned model configured to predict, for an entity and a time period, resources required to perform actions requested by the entity during the time period;
• applying, by the central database system, the machine-learned model to a target entity and a target time period to predict a set of target resources that will be required for the target entity and the target time period;
• and
• modifying, by the central database system, a configuration of the central database system such that the predicted set of target resources are available to the target entity in advance of the target time period.