Interoperable machine learning platform
First Claim
1. A computing device, comprising:
- at least one processor, and a memory connected to the at least one processor, wherein the at least one memory and the at least one processor are respectively configured to store and execute instructions for causing the computing device to perform operations, the operations comprising;
receiving one or more modules of a machine learning workflow;
composing the one or more received modules of the machine learning workflow into at least a portion of a machine learning application; and
processing a machine learning dataset with the composed machine learning application, the processing of the machine learning dataset including;
automatically interfacing the dataset, at runtime, between a first execution environment configured to execute machine learning code in a first programming language and a second execution environment configured to execute code written in a second programming language; and
interfacing metadata schema, at runtime, between the first execution environment configured to execute the machine learning code in the first programming language and the second execution environment configured to execute the code written in the second programming language.
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Abstract
An interoperable platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have few well known plug types. The building blocks can be composed to build complex compositions. Interoperability between data formats, metadata schema and interfaces to machine learning (ML) functions and trained machine learning models can be provided with no loss of information. A cloud runtime environment can be provided in which the composed workflows can be hosted as REST API to run in production.
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Citations
20 Claims
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1. A computing device, comprising:
at least one processor, and a memory connected to the at least one processor, wherein the at least one memory and the at least one processor are respectively configured to store and execute instructions for causing the computing device to perform operations, the operations comprising; receiving one or more modules of a machine learning workflow; composing the one or more received modules of the machine learning workflow into at least a portion of a machine learning application; and processing a machine learning dataset with the composed machine learning application, the processing of the machine learning dataset including; automatically interfacing the dataset, at runtime, between a first execution environment configured to execute machine learning code in a first programming language and a second execution environment configured to execute code written in a second programming language; and interfacing metadata schema, at runtime, between the first execution environment configured to execute the machine learning code in the first programming language and the second execution environment configured to execute the code written in the second programming language. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method, comprising:
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receiving one or more modules of a machine learning workflow; composing the one or more received modules of the machine learning workflow into at least a portion of a machine learning application; and processing a machine learning dataset with the composed machine learning application, the processing of the machine learning dataset including; automatically interfacing the dataset, at runtime, between a first execution environment configured to execute machine learning code in a first programming language and a second execution environment configured to execute code written in a second programming language; and interfacing metadata schema, at runtime, between the first execution environment configured to execute the machine learning code in the first programming language and the second execution environment configured to execute the code written in the second programming language. - View Dependent Claims (10, 11, 12, 13)
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14. A method, including:
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composing the one or more modules of a machine learning workflow into at least a portion of a machine learning application; automatically interfacing a machine learning dataset, while processing the machine learning dataset with the composed machine learning application running on an interoperability platform, between a first execution environment configured to execute machine learning code in a first language for the composed machine learning application and a second execution environment configured to execute code written in a second language for the composed machine learning application; and automatically interfacing metadata, while processing the machine learning dataset with the composed machine learning application running on the interoperability platform, between the first execution environment configured to execute the machine learning code in the first language for the composed machine learning application and the second execution environment configured to execute the code written in the second language for the composed machine learning application. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification