What is Model drift?
Model drift is the gradual decline in a machine learning model's accuracy after deployment because the real-world data it sees has changed from the data it was trained on. It can be caused by shifts in customer behavior, markets, products or the way data is recorded.
Why it matters for your business
A model that worked well at launch can quietly start making poor predictions. Monitoring for drift tells you when to retrain before bad forecasts or decisions affect the business.
A demand forecasting model trained before a new product line launched starts underpredicting orders, and drift alerts prompt the team to retrain it on recent sales.