Systems and methods for generating and deploying machine learning applications

A method for containerized execution of machine learning (ML) software, the method comprising:

• executing, using at least one computer hardware processor, a virtualized ML application program to perform:
• (A) receiving first data comprising a first plurality of values for a first plurality of attributes;
• (B) processing the first data, using a common data attribute schema that indicates a second plurality of attributes, to generate a second plurality of values for at least some of the second plurality of attributes, wherein the at least some of the second plurality of attributes include a first group of attributes, and wherein the second plurality of values includes a first group of attribute values for the first group of attributes;
• (C) identifying, using the common data attribute schema and from among a plurality of pre-existing software codes, first software code implementing a first ML data processing pipeline configured to generate a first group of feature values, for a respective first group of features, from the first group of attribute values;
• (D) processing the first group of attribute values with the first software code to obtain the first group of feature values;
• and
• (E) either:
• (i) providing the first group of feature values as inputs to a machine learning (ML) model for generating corresponding ML model outputs, or (ii) using the first group of feature values to train the ML model.

A system, comprising:

• at least one computer hardware processor;
• and
• at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to execute a virtualized ML application program to perform a method, comprising:
• (A) receiving first data comprising a first plurality of values for a first plurality of attributes;
• (B) processing the first data, in accordance with a common data attribute schema that indicates a second plurality of attributes, to generate a second plurality of values for at least some of the second plurality of attributes, wherein the at least some of the second plurality of attributes include a first group of attributes, and wherein the second plurality of values includes a first group of attribute values for the first group of attributes;
• (C) identifying, using the common data attribute schema and from among a plurality of pre-existing software codes, first software code implementing a first ML data processing pipeline configured to generate a first group of feature values, for a respective first group of features, from the first group of attribute values;
• (D) processing the first group of attribute values with the first software code to obtain the first group of feature values;
• and
• (E) either:
• (i) providing the first group of feature values as inputs to a machine learning (ML) model for generating corresponding ML model outputs, or (ii) using the first group of feature values to train the ML model.

At least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to execute a virtualized ML application program to perform a method, comprising:

• (A) receiving first data comprising a first plurality of values for a first plurality of attributes;
• (B) processing the first data, in accordance with a common data attribute schema that indicates a second plurality of attributes, to generate a second plurality of values for at least some of the second plurality of attributes, wherein the at least some of the second plurality of attributes include a first group of attributes, and wherein the second plurality of values includes a first group of attribute values for the first group of attributes;
• (C) identifying, using the common data attribute schema and from among a plurality of pre-existing software codes, first software code implementing a first ML data processing pipeline configured to generate a first group of feature values, for a respective first group of features, from the first group of attribute values;
• (D) processing the first group of attribute values with the first software code to obtain the first group of feature values;
• and
• (E) either:
• (i) providing the first group of feature values as inputs to a machine learning (ML) model for generating corresponding ML model outputs, or (ii) using the first group of feature values to train the ML model.