The clause describes the step that produces a trained and tested model ready for deployment. It covers feature engineering, the quantitative descriptors the model sees; the selection of an algorithm; the training of one or more candidates; and the selection of the best candidate against the design objectives and the risks identified during development. The framework is explicit that model selection belongs to this stage and must stay separate from evaluation on test data. The public summary of the pipeline names iterative and non-sequential ordering, because analysis can change features, features can change the algorithm choice, and the algorithm can change the labelling need.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.