This paper proposes a method to reduce the training set of a deep learning (DL) model for testing purposes. The reduced training set allows faster model training than the original, full-sized training set and mimics the training characteristics of full-sized model training. The increasing number of software systems based on machine learning systems and learning components requires adequate testing techniques to ensure their functionality. Continuous Integration (CI) is a software development technique foriterative software development. Within CI cycles, changes to the software are integrated into the main software repository, built and tested. One major aspect of modern CI systems is to focus on short cycles times, that provide feedback about the change quality early after submitting the changes to version control. As deep learning (DL), as well as other machine learning systems are more prevalent and central to the software success, also their development and quality assurance is moved within the CI paradigm.