Machine learning (ML) has been developed and growing fast. It also depends on an expert in ML to develop the ML model, i.e. pre-processing, building model, feature engineering, tuning of hyper-parameters, etc. Data scientists have many demanding responsibilities, and challenges. These challenges include the selection of the best algorithm, modification of this algorithm over many iterations, and identification and tuning of the hyper-parameters many times. Despite going through difficult processes, it is worth noting to watch the ML model beat expected accuracy. There is the myth that the developers love to overdo the coding. Many steps that are performed by data scientists are repeatable and time-consuming which makes these steps ideal candidates for automation [1].
Figure 1