  Upon the significant performance of the supervised deep neural networks,
conventional procedures of developing ML system are \textit{task-centric},
which aims to maximize the task accuracy. However, we scrutinized this
\textit{task-centric} ML system lacks in engineering efficiency when the ML
practitioners solve multiple tasks in their domain. To resolve this problem, we
propose an \textit{efficiency-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 \textit{task-centric} paradigm, we discovered that the
\textit{efficiency-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 \textit{efficiency-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.
