Towards an Efficient ML System: Unveiling a Trade-off between Task Accuracy and Engineering Efficiency in a Large-scale Car Sharing Platform

Upon the significant performance of the supervised deep neural networks, conventional procedures of developing ML system are textittask-centric, which aims to maximize the task accuracy. However, we scrutinized this textittask-centric ML system lacks in engineering efficiency when the ML practitioners solve multiple tasks in their domain. To resolve this problem, we propose an textitefficiency-centric ML system that concatenates numerous datasets, classifiers, out-of-distribution detectors, and prediction tables existing in the practitioners' domain into a single ML pipeline. Under various image recognition tasks in the real world car-sharing platform, our study illustrates how we established the proposed system and lessons learned from this journey as follows. First, the proposed ML system accomplishes supreme engineering efficiency while achieving a competitive task accuracy. Moreover, compared to the textittask-centric paradigm, we discovered that the textitefficiency-centric ML system yields satisfactory prediction results on multi-labelable samples, which frequently exist in the real world. We analyze these benefits derived from the representation power, which learned broader label spaces from the concatenated dataset. Last but not least, our study elaborated how we deployed this textitefficiency-centric ML system is deployed in the real world live cloud environment. Based on the proposed analogies, we highly expect that ML practitioners can utilize our study to elevate engineering efficiency in their domain.