Would you say that the technical debt is harder to manage for more complex models (such as neural networks vs decision trees) or is ML debt management a bit unrelated to the model complexity? It it is unrelated, which aspects of a ML pipeline are the most relevant to measuring how hard it is to keep the technical debt low?