Data processing techniques

A non-transitory computer-readable medium storing instructions, the instructions comprising:

• one or more instructions that, when executed by one or more processors, cause the one or more processors to:
• receive first data to be processed;
• receive information identifying a plurality of tasks to be performed to generate second data based on the first data;
• compare a first cost, associated with causing a first task, of the plurality of tasks, to be performed by an automation device, and a second cost associated with causing the first task to be performed using a crowdsourcing platform;
• determine, based on the information identifying the plurality of tasks and comparing the first cost and the second cost, that a first portion of the first task is to be performed by a first crowd associated with the crowdsourcing platform, and that a second portion of the first task is to be performed by a second crowd associated with the crowdsourcing platform, and that a second task, of the plurality of tasks, is to be automatically performed by the automation device;
• receive input specifying an order in which the plurality of tasks is to be performed by each of the automation device, the first crowd, and the second crowd;
• provide, to the crowdsourcing platform and the automation device, at least a portion of the first data;
• cause, based on the order, the first crowd and the second crowd to perform the first task;
• cause, based on the order, the automation device to perform the second task;
• and
• receive, from the automation device and the crowdsourcing platform, the second data,
• the second data being generated based on the crowdsourcing platform performing the first task and based on the automation device performing the second task;
• compare the second data to expected data to determine that at least one of the first crowd or the second crowd correctly performed the first task;
• generate, based on a relationship between the first data and the second data and based on determining that the at least one of the first crowd or the second crowd correctly performed the first task, an automation model,
• where the one or more instructions, that cause the one or more processors to generate the automation model, cause the one or more processors to:
• modify parameters, relating to the relationship between the first data and the second data, for processing, via the automation model, the first task, and
• train a machine learning model, based on the modified parameters, as the automation model;
• and
• provide the automation model to the automation device,
• the automation model causing the automation device to perform the first task.

A method, comprising:

• receiving, by a device, first data to be processed;
• receiving, by the device, information identifying a plurality of tasks to be performed to generate second data based on the first data;
• comparing, by the device, a first cost, associated with causing a first task, of the plurality of tasks, to be performed by an automation device, and a second cost associated with causing the first task to be performed using a crowdsourcing platform;
• determining, by the device, and based on the information identifying the plurality of tasks and based on comparing the first cost and the second cost, that a first portion of the first task is to be performed by a first crowd associated with the crowdsourcing platform, that a second portion of the first task is to be performed by a second crowd associated with the crowdsourcing platform, and that a second task, of the plurality of tasks, is to be performed by the automation device;
• receiving, by the device, input specifying an order in which the plurality of tasks are to be performed by each of the automation device, the first crowd, and the second crowd;
• providing, by the device and to the crowdsourcing platform and the automation device, at least a portion of the first data;
• causing, by the device and based on the order, the first crowd and the second crowd to perform the first task;
• causing, by the device, the automation device to perform the second task;
• receiving, by the device and from the automation device and the crowdsourcing platform, the second data,
• the second data being generated based on the first crowd and the second crowd performing the first task and based on the automation device performing the second task;
• comparing, by the device, the second data to expected data to determine that at least one of the first crowd or the second crowd correctly performed the first task;
• generating, by the device, based on a relationship between the first data and the second data, and based on determining that the at least one of the first crowd or the second crowd correctly performed the first task, an automation model,
• where generating the automation model comprises:
• modifying, by the device, parameters, relating to the relationship between the first data and the second data, for processing, via the automation model, the first task, and
• training, by the device and based on the modified parameters, a machine learning model as the automation model;
• and
• providing, by the device, the automation model to the automation device, the automation model causing the automation device to perform the first task.