5 reasons your ML model isn’t performing well in production

Demand Forecasting Machine Learning (ML) models hold significant potential for retailers to increase revenue and streamline business operations. However, monitoring these models is crucial as they can fail silently without notifying the user of any issues. Common problems include concept drift, where the model's performance degrades due to changes in the real world or data processing pipeline; unintended differences between the data scientist's intended implementation and the engineering team's production model; unexpected data types or missing features; and distribution shifts in one or more of its features. Proper monitoring platforms can help detect these issues early, ensuring models perform as expected.