training deep leaning models the second key point to internalize is that each of the three sets-traininghyperparameter tuningand go-live testing--need to be true representative of the underlying population of data splitting the datasets should take this into consideration for examplethe distribution of examples across the classes should be the same as the underlying population if the data is not true representation (that isif the data is biased in any way)then the performance of the model will not be achieved once the model goes to production the third key point to internalize is that more data is always better for any of the three purposes because the datasets cannot overlap and the overall dataset is limiteda practitioner needs to carefully choose the fraction of the data used of each purpose split or split across trainingvalidationand testing are reasonable choices establishing the achievable limit on the error rate having defined the problem and performance metricsand having procured and split the data into trainingparameter tuningand golive test setthe next step is to establish the achievable limit on the error rate conceptuallythis is the error rate one can hope to achieve given an infinite supply of data and is referred to as the bayes error establishing the limit on the error rate in ai tasks is typically done via proxy-like human labelling or variations on the theme appropriate to the business use case variations may include using an expert on the subject to label the dataa panel of human beingsor panel of experts establishing this limit is quite valuable and well worth the expenditure of human/expert help firstit establishes the best possible results that can be achievedwhichin certain casesmight not be good enough to satisfy the business use case (in which case the problem formulation needs to be rethoughtsecondit tells us how far our current model is from the best achievable results