Manufacturing companies spend significant amount of money and employ several subject-matter experts for their quality control processes. Partially or fully automating these processes using machine learning would lead to significant cost and time saving. This is a feasible opportunity also thanks to the continous development of new machine learning models and to the affordability of cloud services like Azure Machine Learning. This study has the purpose of improving the defect detection deep learning model currently used in production for the visual quality inspection of industrial products. The proposed work explains how the performance can be significantly improved (from 0.20 IoU to 0.60 IoU) by using a more balanced dataset including both detective and non-defective samples and performing an accurate hyper-parameter tuning.