To help fill the information gap on feature engineering, MLaaS hands-on can help and support beginning-to-intermediate data scientists how to work with this widely practiced phenomena. Explaining or gaining common practices and mathematical principles to help engineer features for new data and tasks. MLaaS these days provides full automation of essential, yet time-consuming activities in predictive model construction, such as fast variable selection, variable interaction modeling, and variable transformations or best model selection. Conclusion – At end and at heart we all now the dirty secret no matter how good the algorithm is, no matter how good I as data scientist, no model can perform magic if direction, intension, time and goal is not set.