The evolution of the Machine Learning (ML) has led to the emergence of Transfer Learning (TL) approach, which allows reusing pretrained industrial ML systems after their input data values or even an application domain has changed. In this paper, we investigate the TL process and its impact on the performance of ML-end systems with integrated network facilities. Especially, we focus on ML-systems designed for the classification of image media-files, transmitted over a network. Packet loss in a network is considered as a major input Data Quality (DQ) deterioration factor that can result in ML system classification performance degradation after pretraining on good inputs. To investigate the typical industrial TL process, we study the relationships between the ML model's last layer weights, hyperparameters, and classification performance throughout the retraining process. In addition, we conduct an empirical study to evaluate how the TL affects ML model performance in real application scenarios. For our experiments, we employ real image media-files, and transmit them over a real wireless network with inherent data losses for a classification on a remote ML-end system. According to our results, retraining ML models on corrupted data allows to enhance their robustness to a DQ degradation in the considered image classification scenarios. However, DQ influence on the ML system performance may vary depending on the data and system types.