Cerner Innovation, Inc.

Uttam Ramamurthy

Amit Kumar

Pratik Nath

Shiv Shankar

James Gritter

Pankaj Saxena

Chaithrika Rao

Rahul Jain V

Ashwin Chhetri

Christopher S Finn

David Dellsperger

Stephen Patel

Adam Sabaliauskas

Maulik Gandhi

Brandon Davis

Particular embodiments can update a deployed machine learning model with actual entity data depending on anomalies detected in stream data, which can be stored to a computer object, such as a journal. Various embodiments map particular subsets of a larger pool of raw input data to the particular models that need the input data and store the raw input data to computer objects so that the corresponding machine learning models can make predictions according to any suitable policy or triggering event on any of the data located in the computer objects. Such mapping allows each machine learning model to continuously make predictions based on the data it needs.

Intelligent updating and data processing for deployed machine learning models

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.

Model validation based on sub-model performance

The technology disclosed relates to generating automated test plan scripts. Repository data associated with a test plan script is received. Source code and test plan solution data corresponding to the repository data are analyzed. For example, the repository data may be analyzed using natural language processing and static code analysis. In some embodiments, test steps of the test plan are performed to generate repository data. A suggestion of a reusable component for a particular test step or test validation is provided for one or more of the test steps. A selection of the suggestion of the reusable component for at least one of the test steps is received. An automated test plan script corresponding to the selection is generated.

Auto test generator

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.

System and method for evaluating and deploying data models having improved performance measures

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.

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Karnataka, India

Uttam Ramamurthy