Some tools can be more productive than others. Throughout our experience in implementing an optimal machine-learning pipeline in production, we have learned to appreciate the raw strength of the combination of Savia HANA with Servicios de Datos SAP. The amount of time that can be saved by reformulating the approach and optimizing it to use this combination is significant, compared to a vanilla approach involving usage of Python for data wrangling, cleaning, discovery, and normalization, which are significant aspects of machine learning pipeline development.