Need help some in understanding the difference between the underlying implementation of skew and drift comparator in TFDV.
Need help some in understanding the difference between the underlying implementation of skew and drift comparator in TFDV.
Same thing, the only differences are:
If we set a drift threshold then a previous_statistics should be defined in stats validation. Whereas if we set a skew comparator we need to define a serving_statistics.
If we have an anomaly, with skew comparator we will get an anomaly message saying that there is a difference between training and serving datasets while with drift comparator the message will say that there is a distance between the two datasets.