Data-driven applications based on machine and deep learning are striking roots in the automotive industry. In our talk, we will explore machine learning use cases next to one of the core strategic topics autonomous driving at the BMW Group. We employ novel machine and deep learning pipelines, such as those based on XGBoost and convolutional neural nets, to support a broad range of business requirements. Our setups have proven to be effective in creating value for domains including, but not limited to, vehicle engineering and after-sales. Subsequently, we will outline best practices and lessons learned from both an architectural and methodological perspective.