Keeping ML Models Relevant: Detecting and Preventing Data Drift
Model may perform with excellent accuracy at launch, but six months later its predictions are unreliable. What happened? Key principle: A model is only as good as the data it sees today, not the data it saw six months ago. The likely culprit is data drift, a change in the statistical properties of the input … Read more →