Managing Model Drift Through MLOps

In machine learning (ML), you will hear a lot of talk about MLOps, a discipline borrowed from the more traditional IT DevOps that concentrates on delivering high-quality software development on a rapid and continual basis. What distinguishes MLOps from DevOps and makes it a value-adding enterprise in the ML space is the former's concentration on capabilities specific to the end-to-end ML lifecycle. This includes not only the research and development for the model but also its deployment and post-implementation support. With ML, the latter entails more than making sure your code continues to run smoothly and uninterruptedly but also monitoring and automated retraining. You may think, "But my model is pretty good as it is. Why does it need to be monitored and retrained?"