Fine-tuning suggested mappings with incremental learning
A base AI model is supplied for your App Connect Designer instance, and every 15 days or once a month (as configured for your instance), mapping preferences are collated as training data. This data is then used to retrain the model. Only simple mappings such as one-to-one field mappings between nodes are included in the training data. Complex mappings are discarded. The iterative training is used to incrementally build a personalized model for your Designer instance over time. As new mapping insights are learned, fine-tuning occurs to improve the degree of accuracy of the suggestions that are offered when you build flows.
Disclaimer: You are responsible for any example data (including any personal information) used for, and any decision to proceed with, any automated flows which are suggested for your convenience when IBM® App Connect is used. IBM has no responsibility for any such automated flows and IBM warranties and support will not apply to them; they are used at your risk. IBM might periodically modify the underlying learning models in App Connect through updates, fixes, or patches in order to improve App Connect performance.