A trained model is updated with different training data when training sets were small, when data drift (changed statistical character of production data, such as resolution or class frequency) decays prediction accuracy, in which case retraining uses data that reflects current production, or when concept drift moves the decision boundary (what counts as lawful changes with new law), in which case target variables are relabelled before retraining. Retraining must contend with catastrophic forgetting, where adaptation to new data overwrites earlier knowledge. Continuous learning is the special case of frequent, uninterrupted incremental updates in production; it can involve human steps such as labelling, approving an update or monitoring performance, it requires verification of the system in production and capture of production data into the training set, and it implies learning over time in a dynamic environment while retaining previous knowledge.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.