Machine learning data processing pipeline

A computer-implemented method, comprising:

• generating a user interface configured to receive, from a client, one or more inputs for constructing a data processing pipeline that generates an optimal machine learning model for performing a task;
• responding to the one or more inputs received the client by at least adding, to a graph representative of the data processing pipeline, an orchestrator node, a preparator node, and an executor node, the preparator node configured to generate a training dataset for training a machine learning model to perform the task and a validation dataset for evaluating a performance of the machine learning model trained to perform the task, the executor node configured to execute one or more machine learning trials by at least applying, to the training dataset and the validation dataset, one or more machine learning models having different sets of trial parameters, and the orchestrator node configured to identify, based at least on a result of the one or more machine learning trials, the optimal machine learning model for performing the task;
• and
• generating the optimal machine learning model for performing the task by at least executing, based on the graph, the data processing pipeline including the orchestrator node, the preparator node, and the executor node.

A non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations comprising:

• generating a user interface configured to receive, from a client, one or more inputs for constructing a data processing pipeline that generates an optimal machine learning model for performing a task;
• responding to the one or more inputs received the client by at least adding, to a graph representative of the data processing pipeline, an orchestrator node, a preparator node, and an executor node, the preparator node configured to generate a training dataset for training a machine learning model to perform the task and a validation dataset for evaluating a performance of the machine learning model trained to perform the task, the executor node configured to execute one or more machine learning trials by at least applying, to the training dataset and the validation dataset, one or more machine learning models having different sets of trial parameters, and the orchestrator node configured to identify, based at least on a result of the one or more machine learning trials, the optimal machine learning model for performing the task;
• and
• generating the optimal machine learning model for performing the task by at least executing, based on the graph, the data processing pipeline including the orchestrator node, the preparator node, and the executor node.