A computer system comprising: one or more hardware processors and memory configured to provide computer program instructions to the one or more hardware processors; an adaptable seeding taxonomy that associates a plurality of machine learning scenarios with corresponding subsets of a plurality of variation parameters; a frameset assembly engine of a distributed service for providing synthetic data, the frameset assembly engine configured to utilize the one or more hardware processors to: receive, from an interface of the distributed service for providing synthetic data, a selection of a machine learning scenario from the plurality of machine learning scenarios; retrieve, from the adaptable seeding taxonomy, a subset of variation parameters associated with the selected machine learning scenario by the seeding taxonomy; and cause presentation of the subset of variation parameters, on the interface, as suggested variation parameters for the selected machine learning scenario; wherein the adaptable seeding taxonomy is configured to update based on a determined difference between selected variation parameters for a subsequent corresponding frameset generation request and the suggested variation parameters in the adaptable seeding taxonomy associated with the selected machine learning scenario. one or more hardware processors and memory configured to provide computer program instructions to the one or more hardware processors; an adaptable seeding taxonomy that associates a plurality of machine learning scenarios with corresponding subsets of a plurality of variation parameters; a frameset assembly engine of a distributed service for providing synthetic data, the frameset assembly engine configured to utilize the one or more hardware processors to: receive, from an interface of the distributed service for providing synthetic data, a selection of a machine learning scenario from the plurality of machine learning scenarios; retrieve, from the adaptable seeding taxonomy, a subset of variation parameters associated with the selected machine learning scenario by the seeding taxonomy; and cause presentation of the subset of variation parameters, on the interface, as suggested variation parameters for the selected machine learning scenario; wherein the adaptable seeding taxonomy is configured to update based on a determined difference between selected variation parameters for a subsequent corresponding frameset generation request and the suggested variation parameters in the adaptable seeding taxonomy associated with the selected machine learning scenario. receive, from an interface of the distributed service for providing synthetic data, a selection of a machine learning scenario from the plurality of machine learning scenarios; retrieve, from the adaptable seeding taxonomy, a subset of variation parameters associated with the selected machine learning scenario by the seeding taxonomy; and cause presentation of the subset of variation parameters, on the interface, as suggested variation parameters for the selected machine learning scenario; wherein the adaptable seeding taxonomy is configured to update based on a determined difference between selected variation parameters for a subsequent corresponding frameset generation request and the suggested variation parameters in the adaptable seeding taxonomy associated with the selected machine learning scenario.