training deep leaning models mathematicallywe can define mape as mape yi ^ = yi while being highly interpretablemape suffers when dealing with small differences the percentage differences of small deviations often result in large mapewhich could lead to misleading results supposefor examplethat we are predicting the number of days sales will be observed for given store and the target values range from to when the actual value is and the predicted value is the mape is %whereas when the actual value is and the predicted value is the mape is data procurement data procurement is the process of collecting data for building model according to problem statement data procurement can involve collecting old (already generateddata from production systemscollecting live data from production systemsandin many casescollecting data labeled by human operators (via crowdsourcing or internal operations teamsin our running example of product categorizationproduct titlesimagesdescriptionsetc would need to be collected from company catalogueand labeled data could be generated using crowdsourcing we might also want to collect click data and sales to determine the popular products (misclassification in these cases would be costly data procurement typically happens in conjunction with the process of defining the problem statement and success metrics it is imperative that practitioner play an active role in the data procurement process typicallyin an industry settingdata procurement is fairly time-consuming and painful process subtle errors in data procurement can derail project at later stage