Our study demonstrated ML acceleration of the electrolyte optimisation and the cycling task by early prediction of the EOL. This shows that in MAPs centred around FINALES, the overall optimisation task as well as the tasks of individual tenants can be accelerated using ML. Besides, this study demonstrated the ability of our MAP to handle tenants with significantly different rates of data generation. To accommodate these differences, it is inevitable to allow each tenant to operate at its own schedule in an asynchronous fashion. Marking requests as reserved once they are processed enables a distribution of the tasks among faster and slower tenants providing the same quantity. The modular design of the FINALES-based MAP leads us to expect seamless connection of new tenants and, hence, scalability to larger MAPs. We anticipate that the multi-modal and multi-fidelity nature of the data generated in such MAPs benefits the robustness of models trained with the data. Future studies deploying our MAP concept will additionally benefit from the possibility to prepopulate a database with selected existing data. This is powered by FINALES accepting unsolicited results which do not reference to a request. Starting from existing data will reduce the need for initial random experiments and therefore accelerate optimisation tasks.