Machine learning model with conditional execution of multiple processing tasks

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

• receiving input data at a trained machine learning model that includes a common part and task-specific parts;
• receiving an execution instruction at the trained machine learning model that identifies one or more processing tasks to be performed;
• processing the input data using the common part of the trained machine learning model to generate intermediate data;
• and
• processing the intermediate data using one or more of the task-specific parts of the trained machine learning model that correspond to the one or more processing tasks to be performed that are identified by the execution instruction to generate one or more outputs, wherein processing the intermediate data using the one or more of the task-specific parts of the trained machine learning model based on the execution instruction includes loading only the one or more of the task-specific parts of the trained machine learning model that are identified by the execution instruction.

A system, comprising:

• a memory;
• and
• a processor that is configured to execute instructions that are stored in the memory, wherein the instructions, when executed by the processor, cause the processor to:
• receive input data at a trained machine learning model that includes a common part and task-specific parts;
• receive an execution instruction at the trained machine learning model that identifies one or more processing tasks to be performed;
• process the input data using the common part of the trained machine learning model to generate intermediate data;
• and
• process the intermediate data using one or more of the task-specific parts of the trained machine learning model that correspond to the one or more processing tasks to be performed that are identified by the execution instruction to generate one or more outputs, wherein processing the intermediate data using the one or more of the task-specific parts of the trained machine learning model based on the execution instruction includes loading only the one or more of the task-specific parts of the trained machine learning model that are identified by the execution instruction.