One of the main concerns when dealing with multiple data sources is maintaining semantic consistency of the data exchanged. Semantic interoperability involves the use of common information models, ontologies, and standards that enable different systems to understand and interpret the data in the same way. It ensures that the meaning of the data is preserved across different contexts and platforms, allowing for accurate data integration and transformation and accelerating the ML model training process.