Machine learning method has been suggested and applied to the parameter estimation in binary microlensing events, as a replacement of the time-consuming, sampling-based approach. However, the equal-step time series that is required by existing attempts are rarely realized in ground-based surveys. In this work, we apply the neural controlled differential equation (neural CDE) to handle microlensing light curves of realistic data quality. Our method can infer binary parameters efficiently and accurately out of light curves with irregular time-steps (including large gaps). Our work also demonstrates the power of neural CDE and other advanced machine learning methods in identifying and characterizing transient events in ongoing and future ground-based time domain surveys, given that it is common for astronomical time series from the ground to have irregular sampling and data gaps. The extended journal paper can be found at arXiv:2206.08199.