In this work, we have successfully upgraded our atomistic ML suite PiNN to include equivariant features in the PiNet2 architecture. Through careful tests and benchmarks using public available datasets of small molecules, crystalline materials and liquid systems, it is found that PiNet2-P3 shows a universal and significant improvement with moderate computational overhead as compared to PiNet. This does not only apply to energy and force predictions but also to dipole, charge and polarizability predictions, as demonstrated by the PiNet2dipole and PiNet2-χ modules. New versions of the PiNN code are freely accessible and kept updated at with documentation and tutorials. Since the introduction of the idea to couple finite-field methods and atomistic ML, a number of implementations have been developed and applied to simulate insulating condensed-phase systems (e.g. liquid water) with field-dependent MLPs . In the meantime, the integration of atomistic ML and molecular simulation for modelling metallic electrodes has also been made possible, as exemplified by the development of PiNNwall and other upcoming and interesting "QM/MM"type implementations with ML ingredients. Very recently, finite-field DFTMD simulations have been demonstrated for modeling polarized Au(100) and Au(111) electrodes immersed in electrolyte solution. This long-awaited endeavor opens the door for joining these two streams in the near future for a full integration and ML description of reactive and polarizable electrochemical interfaces. With the equivariant neural network suite PiNN introduced here, we are ready for this challenge and anticipating many interesting applications to realistic electrochemical systems coming out soon.