Abstract: Methods, systems, and computer-readable media are disclosed herein for a concurrent comparative tool for assessing sub-models of a data model pipeline in a deployed or pre-deployment environment. The tool may compute a plurality of performance measures that quantitatively assess the performance of each sub-model in the data model pipeline based on a configuration file that facilitates validation of the technological performance and predictive accuracy of the sub-model. Additionally, multiple versions of a sub-model deployed in similar data model pipelines, or in a pre-deployment environment, may be comparatively evaluated. A leading version of the sub-model may be identified and deployed.

Abstract: Methods, systems, and computer-readable media are disclosed herein for a concurrent comparative tool for assessing multiple versions of a data model in a pre-deployment environment to ensure that any subsequently deployed version will perform than a current version of the data model. In aspects, the tool extracts observed and predictive data for various versions and comparatively evaluates the performance measures of each version. The performance measures can be validated across the versions to determine and select a leading version that has demonstrated improve technological performance and predictive accuracy. The leading version can be deployed.

Abstract: Methods, systems, and computer-readable media are disclosed herein for a concurrent comparative tool for assessing multiple versions of a data model in a pre-deployment environment to ensure that any subsequently deployed version will perform than a current version of the data model. In aspects, the tool extracts observed and predictive data for various versions and comparatively evaluates the performance measures of each version. The performance measures can be validated across the versions to determine and select a leading version that has demonstrated improve technological performance and predictive accuracy. The leading version can be deployed.

Abstract: Methods, systems, and computer-readable media are disclosed herein for a concurrent comparative tool for aggregating computationally generated performance reports a machine-learning data model pipeline for technological performance and/or predictive accuracy. The tool may acquire multiple performance measure reports that that quantitatively asses the performance of a model pipeline based on a configuration file that facilitates validation of the technological performance and predictive accuracy of the model. Additionally, when the conditions defined by the configuration file are met for of the performance measure reports the tool may broadcast a notification to a predetermined system.

Abstract: Methods, systems, and computer-readable media are disclosed herein for a concurrent comparative tool for assessing sub-models of a data model pipeline in a deployed or pre-deployment environment. The tool may compute a plurality of performance measures that quantitatively asses the performance of each sub-model in the data model pipeline based on a configuration file that facilitates validation of the technological performance and predictive accuracy of the sub-model. Additionally, multiple versions of a sub-model deployed in similar data model pipelines, or in a pre-deployment environment, may be comparatively evaluated. A leading version of the sub-model may be identified and deployed.

Abstract: The technology disclosed relates to generating automated test plan scripts. A selection of a first test plan to automate is received. Test scripts and data from a repository are retrieved and the test scripts and the data correspond to the first test plan. Test steps of the first test plan are performed. A prediction of a reusable component for a particular test step or test validation is provided for each of the test steps. A selection of at least one prediction for at least one of the test steps is received. An automated test plan script corresponding to the selection of the at least one prediction is generated.

Abstract: The technology disclosed relates to generating automated test plan scripts. A selection of a first test plan to automate is received. Test scripts and data from a repository are retrieved and the test scripts and the data correspond to the first test plan. Test steps of the first test plan are performed. A prediction of a reusable component for a particular test step or test validation is provided for each of the test steps. A selection of at least one prediction for at least one of the test steps is received. An automated test plan script corresponding to the selection of the at least one prediction is generated.