Enhancing Machine Learning Assurance with Portend

Data drift occurs when machine learning (ML) models are deployed in environments that no longer resemble the data on which they were trained. As a result of this change, model performance can deteriorate. In this blog post from the Software Engineering Institute (SEI) at Carnegie Mellon University, we introduce Portend, a new open source toolset from the SEI that simulates data drift in ML models and identifies the proper metrics to detect drift in production environments. Portend can also produce alerts if it detects drift, enabling users to take corrective action and enhance ML assurance. This post explains the toolset architecture and illustrates an example use case.