Companies have always been very interested in expanding and improving their decision-making principles. In the past, business decisions were largely based on the experience of proven employees and gut instincts. Over time, accounting systems have become easier and better at revising past business data for interesting patterns and deviations. The problem with these analyzes, however, was that they were always focused on the past. More than one "Lessons Learned" was not in it.

When businesses identify a problem which can be solved by machine learning, they brief the data analysts and scientists to create a predictive analytics solution. In many cases, the turnaround period for delivering a solution is quite long. Even for seasoned data scientists, switching machine learning models that can accurately forecast the outcomes is always challenging and time-consuming. The complex workflow involved in machine learning units have several stages. Some of the substantial measures include data acquisition, information mining, feature engineering, design selection, experimentation and prediction.