MACHINE LEARNING SYSTEM FLOW AUTHORING TOOL
First Claim
1. A computer-implemented method, comprising:
- accessing a workflow text string associated with a machine learning workflow and an operator text string associated with at least a data processing operator type;
parsing the workflow text string to generate an interdependency graph of one or more data processing operators, wherein at least one of the data processing operators is an instance of the data processing operator type;
parsing the operator text string to identify operator attributes associated with the data processing operator type, wherein the operator attributes comprise an input schema and an output schema, wherein the input schema or the output schema includes a summary generation schema; and
wherein the operator text string identifies computational logic that is executable on a single host operating environment as a single unit;
scheduling the machine learning workflow for execution based on the interdependency graph; and
generating a summary of a resulting output or an input parameter to the machine learning workflow based the summary generation schema.
3 Assignments
0 Petitions
Accused Products
Abstract
Some embodiments include a workflow authoring tool that accesses a text string representation of a workflow and a text string representation of at least a data processing operator type. The workflow authoring tool enables definition of one or more data processing operator types that can be referenced in defining the machine learning workflow. When scheduling a workflow, the text string representation of the workflow can be parsed and traversed to generate an interdependency graph of one or more data processing operators. The text string representation of the data processing operator type can identify operator attributes associated with the data processing operator type.
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Citations
20 Claims
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1. A computer-implemented method, comprising:
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accessing a workflow text string associated with a machine learning workflow and an operator text string associated with at least a data processing operator type; parsing the workflow text string to generate an interdependency graph of one or more data processing operators, wherein at least one of the data processing operators is an instance of the data processing operator type; parsing the operator text string to identify operator attributes associated with the data processing operator type, wherein the operator attributes comprise an input schema and an output schema, wherein the input schema or the output schema includes a summary generation schema; and
wherein the operator text string identifies computational logic that is executable on a single host operating environment as a single unit;scheduling the machine learning workflow for execution based on the interdependency graph; and generating a summary of a resulting output or an input parameter to the machine learning workflow based the summary generation schema. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A computer readable data memory storing computer-executable instructions that, when executed by a computer system, cause the computer system to perform a computer-implemented method, the instructions comprising:
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instructions for receiving an operator definition of an operator type associated with operator attributes that identify computational logics that is executable on a single host operating environment as a single unit, an input schema, and an output schema, wherein the input schema or the output schema includes a summary generation schema; instructions for receiving a text string representation of a workflow including one or more references to one or more data processing operators of one or more operator types including the operator type; instructions for traversing through the text string representation of the workflow to determine a set of expected promises made between the operator types, wherein the expected promises indicate interdependencies between the operator types; and instructions for scheduling execution of the workflow by at least assigning executing instances of the operator types to one or more computing environments and passing data between the computing environments based on the interdependencies. - View Dependent Claims (19)
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20. A computer system, comprising:
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an operator repository configured to store serialized definitions of one or more operators; a workflow authoring tool configured to receive a workflow text string associated with a machine learning workflow and an operator text string associated with at least a data processing operator type; an execution scheduler engine configured to parse the workflow text string to generate an interdependency graph of one or more data processing operators and to parse the operator text string to identify operator attributes associated with the data processing operator type; wherein the operator attributes comprise an input schema and an output schema, wherein the input schema or the output schema includes a summary generation schema; and
wherein the operator text string identifies computational logics that is executable on a single host operating environment as a single unit; andwherein the execution scheduler engine is further configured to schedule the machine learning workflow for execution based on the interdependency graph; and an experiment analytic interface configured to generate a summary of a resulting output or an input parameter to the machine learning workflow based the summary generation schema.
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Specification