Improving Automated Retraining of Machine-Learning Models

Machine-learning (ML) models are increasingly used to support mission and business goals, ranging from determining reorder points for supplies, to event triaging, to suggesting courses of action. However, ML models degrade in performance after being put into production, and must be retrained, either automatically or manually, to account for changes in operational data with respect to training data. Manual retraining is effective, but costly, time consuming, and dependent on the availability of trained data scientists. Current industry practice offers MLOps as a potential solution to achieve automatic retraining.