PurpleCube’s AI Reference Models help you maintain high quality machine learning (ML) models by automatically detecting and alerting on inaccurate predictions from models deployed in production.

PurpleCube’s AI Reference Models constantly monitor model performance characteristics such as accuracy which measures the number of correct predictions compared to the total number of predictions so you can take action to address anomalies.

The accuracy of ML models can deteriorate over time, a phenomenon known as model drift. Many factors can cause model drift such as changes in model features. The accuracy of ML models can also be affected by concept drift, the difference between data used to train models and data used during inference.

PurpleCube’s AI Reference Models helps you maintain high quality ML models by detecting model and concept drift in real-time, and sending you alerts so you can take immediate action.

Model and concept drift are detected by monitoring the quality of the model based on independent and dependent variables. Independent variables (also known as features) are the inputs to an ML model, and dependent variables are the outputs of the model. For example, with an ML model predicting a bank loan approval, independent variables could be age, income, and credit history of the applicant, and the dependent variable would be the actual result of the loan application.