Cerner Innovation, Inc.

James Gritter

Uttam Ramamurthy

Christopher S Finn

Pankaj Saxena

Rahul Jain V

Ashwin Chhetri

Maulik Gandhi

David Dellsperger

Brandon Davis

Stephen Patel

Adam Sabaliauskas

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 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

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Parkville, MO, United States of America

James Gritter