Distinguishing data drift from concept drift, monitoring without labels, alerting on the right signal, and deciding when a retrain is warranted.
3 questions in MLOps & ML Platform.
Data drift is a change in the distribution of the inputs; concept drift is a change in the relationship between inputs and the outcome. You can detect data drift from inputs alone, concept drift needs labels or a performance signal, and only concept drift necessarily invalidates what the model learned.
Retrain when there is evidence the learned relationship no longer holds and you have data that reflects the new one. A fixed schedule is a fallback for when you cannot measure that, not a decision, and a drift alert is as likely to mean an upstream pipeline broke as it is to mean the world moved.
Monitor what is available immediately: input distributions and data quality, the prediction distribution, and proxy outcomes that correlate with the label but arrive sooner. Do all of it per segment, because a small segment failing badly is invisible in an aggregate.