# US 2022/0036232 A1 · Technology for optimizing artificial intelligence pipelines > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0036232 A1 - Document ID: US20220036232A1 - Status: Published application - Kind: A1 - Application number: 16/941,615 - Filed: 2020-07-29 - Published: 2022-02-03 - Earliest claimed: 2020-07-29 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Dhavalkumar C. Patel; Shrey Shrivastava; Wesley M. Gifford - Web page: https://patentgrep.com/patent/US20220036232A1 ## CPC - G06N 20/00: Machine learning - G06F 8/60: Software deployment - G06F 8/71: Version control (security arrangements therefor G06F21/57); Configuration management - G06F 9/3867: {using instruction pipelines} - G06F 8/72: Code refactoring - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Machine logic to change steps included in and/or parameters/parameter value used in artificial intelligence (“AI”) pipelines. For example, the machine logic may control what types of data (for example, sensor data) are received by the AI pipeline and/or have the data is culled in the pipeline prior to application of a machine learning and/or artificial intelligence algorithm. ## Claims ### Claim 1 A computer-implemented method (CIM) for use with an original artificial intelligence pipeline (AIP), the CIM comprising: orchestrating, by a pipeline deployment tool, an examination of the original AIP to yield a set of pipeline revision(s); producing, by the pipeline deployment tool, a revised version of the AIP, along with associated metadata; and deploying the revised version of the AIP. ### Claim 2 (depends on 1) The CIM of claim 1 further comprising: refactoring the original AIP for deployment purposes to ensure efficiency without losing model fidelity. ### Claim 3 (depends on 1) The CIM of claim 1 wherein the production of the revised version of the AIP, along with associated metadata, includes: examining, by a pipeline inspection tool, a plurality of existing trained AI pipelines; and identifying, by the pipeline inspection tool, step(s) of the original AIP where potential revisions could occur; evaluating, by a revision planner, potential candidate revision(s); and identifying, by the revision planner, which potential candidate revision(s) should be made given available resources, and the order in which those potential candidate revision(s) should proceed. ### Claim 4 (depends on 1) The CIM of claim 1 further comprising: determining, by a pipeline step revision component, how to revise a first step in the original AIP according to a known set of step types and rules which can be applied to reduce both input requirements and model complexity; and examining inputs and outputs of the first step to infer potential reductions in either input or model complexity, without understanding specifics of the first step. ### Claim 5 (depends on 1) The CIM of claim 1 further comprising: propagating, by a revision propagator component, the revised version of the AIP, along with information about the revision, to propagate changes to ensure consistency and correctness of the AIP. ### Claim 6 (depends on 1) The CIM of claim 1 further comprising: comparing the revised version of the AIP with the original AIP to determine a fidelity level value characterizing a level of fidelity with which the revised version of the AIP reproduces the original AIP. ### Claim 7 A computer program product (CPP) for use with an original artificial intelligence pipeline (AIP), the CPP comprising: a set of storage device(s); and computer code stored on the set of storage device(s), with the computer code including data and instructions for causing a processor(s) set to perform the following operations: orchestrating, by a pipeline deployment tool, an examination of the original AIP to yield a set of pipeline revision(s), producing, by the pipeline deployment tool, a revised version of the AIP, along with associated metadata, and deploying the revised version of the AIP. ### Claim 8 (depends on 7) The CPP of claim 7 wherein the computer code further includes data and instructions for causing the processor(s) set to perform the following operation(s): refactoring the original AIP for deployment purposes to ensure efficiency without losing model fidelity. ### Claim 9 (depends on 7) The CPP of claim 7 wherein the production of the revised version of the AIP, along with associated metadata, includes: examining, by a pipeline inspection tool, a plurality of existing trained AI pipelines; and identifying, by the pipeline inspection tool, step(s) of the original AIP where potential revisions could occur; evaluating, by a revision planner, potential candidate revision(s); and identifying, by the revision planner, which potential candidate revision(s) should be made given available resources, and the order in which those potential candidate revision(s) should proceed. ### Claim 10 (depends on 7) The CPP of claim 7 wherein the computer code further includes data and instructions for causing the processor(s) set to perform the following operation(s): determining, by a pipeline step revision component, how to revise a first step in the original AIP according to a known set of step types and rules which can be applied to reduce both input requirements and model complexity; and examining inputs and outputs of the first step to infer potential reductions in either input or model complexity, without understanding specifics of the first step. ### Claim 11 (depends on 7) The CPP of claim 7 wherein the computer code further includes data and instructions for causing the processor(s) set to perform the following operation(s): propagating, by a revision propagator component, the revised version of the AIP, along with information about the revision, to propagate changes to ensure consistency and correctness of the AIP. ### Claim 12 (depends on 7) The CPP of claim 7 further comprising: comparing the revised version of the AIP with the original AIP to determine a fidelity level value characterizing a level of fidelity with which the revised version of the AIP reproduces the original AIP. ### Claim 13 (depends on 7) The CPP of claim 7 further comprising the processor(s) set, wherein the CPP is in the form of a computer system (CS). ### Claim 14 (depends on 13) The CS of claim 13 wherein the computer code further includes data and instructions for causing the processor(s) set to perform the following operation(s): refactoring the original AIP for deployment purposes to ensure efficiency without losing model fidelity. ### Claim 15 (depends on 13) The CS of claim 13 wherein the production of the revised version of the AIP, along with associated metadata, includes: examining, by a pipeline inspection tool, a plurality of existing trained AI pipelines; identifying, by the pipeline inspection tool, step(s) of the original AIP where potential revisions could occur; evaluating, by a revision planner, potential candidate revision(s); and identifying, by the revision planner, which potential candidate revision(s) should be made given available resources, and the order in which those potential candidate revision(s) should proceed. ### Claim 16 (depends on 13) The CS of claim 13 wherein the computer code further includes data and instructions for causing the processor(s) set to perform the following operation(s): determining, by a pipeline step revision component, how to revise a first step in the original AIP according to a known set of step types and rules which can be applied to reduce both input requirements and model complexity; and examining inputs and outputs of the first step to infer potential reductions in either input or model complexity, without understanding specifics of the first step. ### Claim 17 (depends on 13) The CS of claim 13 wherein the computer code further includes data and instructions for causing the processor(s) set to perform the following operation(s): propagating, by a revision propagator component, the revised version of the AIP, along with information about the revision, to propagate changes to ensure consistency and correctness of the AIP. ### Claim 18 A computer-implemented method (CIM) comprising: receiving computer code corresponding to an original version of a machine learning module (ML mod) structured and/or programmed to: (i) receive input data that includes X input parameter values respectively corresponding to X parameters, where X is an integer greater than one, (ii) to select Y input parameter values of the X input parameters to obtain Y selected/extracted parameter values, where Y is an integer less than or equal to X, and (iii) apply an ML algorithm, which has been developed, at least in part, by ML, to the Y selected/extracted parameter values to obtain a recommendation; performing feature selection, by machine logic, to obtain updated value(s) for at least one of the following variables: X and/or Y; and revising, by machine logic, the original version of the ML mod to obtain an updated version of the ML mod that is characterized by the updated value(s) for X and/or Y. ### Claim 19 (depends on 18) The CIM of claim 18 wherein the performance of feature selection decreases the value of X such that the updated version of the ML mod is programmed to accept fewer input parameter values than the original version of the ML mod. ### Claim 20 (depends on 18) The CIM of claim 18 wherein the performance of feature selection decreases the value of Y such that the updated version of the ML mod is programmed to use fewer selected/extracted parameter values in the ML algorithm than the original version of the ML mod.