Comment by zv

Sorry, I disagree with you. Let's take forecasting. This is a _very_ hard topic and being applied correctly using actuarial methods it will beat ML. To give you some example, you cannot take sales data for IPhone X and predict its future sales. Reason is simple - market will change, IPhone 11 will come out cannibalizing IPhone X sales. So, you need to have 2 more variables into play. One is the lifetime of a product (approximated) and second is, you need to "put in one basket" several products. Having this, you can predict your sales for some specific product group, no magic ML needed. However this requires you to define "similar products". This is hard because currently it is done semi-automatically using human labor. In fact ML is actually the best one to automagically categorize products into groups, however I have not seen any implementations. To summarize, ML do has value, however not where everybody is pointing out.