  Machine learning is being widely adapted in industrial applications owing to
the capabilities of commercially available hardware and rapidly advancing
research. Volkswagen Financial Services (VWFS), as a market leader in vehicle
leasing services, aims to leverage existing proprietary data and the latest
research to enhance existing and derive new business processes. The
collaboration between Information Systems and Machine Learning Lab (ISMLL) and
VWFS serves to realize this goal. In this paper, we propose methods in the
fields of recommender systems, object detection, and forecasting that enable
data-driven decisions for the vehicle life-cycle at VWFS.
