CPC G06F 9/5044 (2013.01) [G06F 9/4881 (2013.01); G06F 9/5027 (2013.01); G06F 9/5055 (2013.01); G06F 9/542 (2013.01); G06F 16/906 (2019.01); G06F 16/907 (2019.01); G06F 16/9038 (2019.01); G06F 18/217 (2023.01); G06F 18/2148 (2023.01); G06F 18/2178 (2023.01); G06F 18/24 (2023.01); G06N 20/00 (2019.01); G06V 10/774 (2022.01); G06V 10/96 (2022.01); G06V 20/647 (2022.01); H04L 67/63 (2022.05); G06F 9/3877 (2013.01); G06V 40/172 (2022.01)] | 20 Claims |
1. A computer system comprising:
one or more hardware processors and memory configured to provide computer program instructions to the one or more hardware processors;
a frameset assembly engine configured to utilize the one or more hardware processors to:
receive, from a distributed Synthetic Data as a Service (SDaaS) interface, a selection of a machine learning scenario from a plurality of machine learning scenarios, wherein the SDaaS interface is associated with SDaaS distributed computing service operations that are based on a service-oriented architecture that supports SDaaS machine-learning training service operations while abstracting underlying SDaaS distributed computing service operations that managed via an SDaaS distributed computing service;
retrieve, from a seeding taxonomy that associates the plurality of machine learning scenarios with corresponding proper subsets of a plurality of variation parameters, a relevant proper subset of the plurality of variation parameters associated with the selected machine learning scenario by the seeding taxonomy,
wherein the seeding taxonomy comprises a plurality of previously identified mappings of machine learning scenarios to corresponding proper subsets of the plurality of variation parameters;
cause the SDaaS interface to prompt for a selected subset of the plurality of variation parameters in association with presenting a representation of the relevant proper subset of the plurality of variation parameters as suggested variation parameters for the selected machine learning scenario;
receive, from the SDaaS interface, input identifying the selected subset of the plurality of variation parameters; and
generate, using the selected subset of the plurality of variation parameters, a training dataset as a frameset package associated with training the machine learning model for the selected machine learning scenario,
wherein generating the frameset package is based on intrinsic-parameter variation and extrinsic-parameter variation of the selected subset of the plurality of variation parameters, wherein intrinsic-parameter variation and extrinsic-parameter variation provide programmable machine-learning data representations of synthetic data assets.
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