After a machine learning model has been deployed to production, its performance is continuously monitored. Over several months, it's observed that the model's accuracy on new, incoming data is gradually declining. The team decides they need to periodically retrain the model with fresh data and potentially adjust its parameters to maintain performance. These ongoing activities of monitoring, retraining, and updating deployed models fall under which stage of the machine learning lifecycle?