Techniques for adaptive pipelining composition for machine learning (ML)

A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a data processing apparatus to perform operations executed as a background process of a machine learning application, the operations comprising:

• identifying a first machine learning model, wherein the first machine learning model utilizes one or more first parameters to identify and define properties of the first machine learning model;
• generating a first result for the first machine learning model using a first data set as an input to the first machine learning model;
• identifying one or more metrics for the first machine learning model, wherein the metrics determine a performance of the first machine learning model;
• generating a second result for the first machine learning model using a second data set as the input to the first machine learning model, wherein the second data set comprises ground truth data;
• comparing the first result with the second result to calculate a first scoring;
• analyzing one or more first parameters to identify an ontology for the first machine learning model;
• using the ontology to identify a second machine learning model based at least in part on comparing first metadata of the second machine learning model with second metadata for the first machine learning model;
• testing the second machine learning model on the first data set to produce a third result;
• generating a fourth result for the second machine learning model using the second data set as the input to the second machine learning model;
• comparing the third result to the fourth result to generate a second scoring;
• and
• based on the second scoring being less than the first scoring, replacing the first machine learning model with the second machine learning model for the machine learning application.

A system for executed as a background process of a machine learning application, comprising:

• one or more data processors;
• and
• a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations comprising:
• identifying a first machine learning model, wherein the first machine learning model utilizes one or more first parameters to identify and define properties of the first machine learning model;
• generating a first result for the machine learning model using a first data set as an input to the first machine learning model;
• identifying one or more metrics for the first machine learning model, wherein the metrics determine a performance of the first machine learning model;
• generating a second result for the first machine learning model using a second data set as the input to the first machine learning model, wherein the second data set comprises ground truth data;
• comparing the first result to the second result to calculate a first scoring;
• analyzing the one or more first parameters to identify an ontology for the first machine learning model;
• using the ontology to identify a second machine learning model based at least in part on comparing first metadata of the second machine learning model with second metadata for the first machine learning model;
• testing the second machine learning model on the first data set to produce a third result;
• generating a fourth result for the second machine learning model using the second data set as the input to the second machine learning model;
• comparing the third result to the fourth result to generate a second scoring;
• and
• based on the second scoring being less than the first scoring, replacing the first machine learning model with the second machine learning model for the machine learning application.