Real-world uses

Created for this library

1. 1.

   An ML platform team audits training-serving skew by comparing feature distributions in offline training data against production logs.

2. 2.

   A retail recommender team runs automated training-serving skew checks so production drift is caught before it harms revenue.

3. 3.

   A risk modeling team monitors training-serving skew on key features so changes upstream of the model trigger reviews automatically.