Modern cloud-software providers, such as Salesforce.com, increasingly adopt large-scale continuous integration envi-ronments. In such environments, assuring high developer productivity is strongly dependent on conducting testing efficiently and effectively. Specifically, to shorten feedback cycles, test prioritization is popularly used as an optimiza-tion mechanism for ranking tests to run by their likelihood of revealing failures. To apply test prioritization in indus-trial environments, we present a novel approach (tailored for practical applicability) that integrates multiple existing techniques via a systematic framework of machine learning to rank. Our initial empirical evaluation on a large real-world dataset from Salesforce.com shows that our approach significantly outperforms existing individual techniques.