Hamed ZITOUN on LinkedIn: #machinelearning

When data quality is fine, there are two usual suspects: data drift or concept drift. Data Drift -- The input data has changed. The distribution of the variables is meaningfully different. As a result, the trained model is not relevant for this new data. Concept Drift -- In contrast to the data drift, the distributions might even remain the same.