Carnegie Mellon: Portend Toolset Creates Guardrails for Machine Learning Data Drift
December 19, 2024
December 19, 2024
PITTSBURGH, Pennsylvania, Dec. 19 -- Carnegie Mellon University's Software Engineering Institute issued the following news:
Machine learning (ML) models are powerful but brittle components of intelligent software systems. Developers strive to select model training data that is as close as possible to the deployment environment. If the input data from the deployment environment drifts away from the training data, the model can produce incorrect outputs, endangering the system's missi . . .
Machine learning (ML) models are powerful but brittle components of intelligent software systems. Developers strive to select model training data that is as close as possible to the deployment environment. If the input data from the deployment environment drifts away from the training data, the model can produce incorrect outputs, endangering the system's missi . . .