Runtime estimation for machine learning data processing pipeline

A computer-implemented method, comprising:

• receiving, from a client device, one or more inputs for constructing a data processing pipeline configured to implement a process to generate a machine learning model for performing a task associated with an input dataset, the process including a plurality of machine learning trials, each of the plurality of machine learning trials applying, to a training dataset, a different type of machine learning model and/or a different set of trial parameters, and the machine learning model being generated based at least on a result of the plurality of machine learning trials;
• determining a first runtime for the process to generate the machine learning model;
• and
• executing, based at least on the first runtime not exceeding an available time budget, the process to generate the machine learning model.

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

• receiving, from a client device, one or more inputs for constructing a data processing pipeline configured to implement a process to generate a machine learning model for performing a task associated with an input dataset, the process including a plurality of machine learning trials, each of the plurality of machine learning trials applying, to a training dataset, a different type of machine learning model and/or a different set of trial parameters, and the machine learning model being generated based at least on a result of the plurality of machine learning trials;
• determining a first runtime for the process to generate the machine learning model;
• and
• executing, based at least on the first runtime not exceeding an available time budget, the process to generate the machine learning model.