How to Detect and Overcome Model Drift in MLOps

Machine learning (ML) is widely regarded as the cornerstone of digital transformation, yet ML models are the most susceptible to the changing dynamics of a digital landscape. ML models are defined and optimized by the variables and parameters available at the time period in which they are created. Let us look at the case of an ML model created to track spam emails based on a generalized template of spam emails that may have been proliferating at the time. With this baseline in place, the ML model is able to identify and stop these sorts of emails, thus preventing potential phishing attacks. However, as the threat landscape changes and cybercriminals become smarter, more sophisticated and realistic emails have replaced the old ones.