Question

A financial services firm has deployed a custom demand-forecasting model built in Microsoft Foundry into its production Dynamics 365 Supply Chain environment. Over the past quarter, forecast accuracy has degraded steadily even though no code or model version has changed. The architect must design the ALM process so that the model is retrained and re-promoted at the right time rather than on a fixed calendar schedule. Which approach best addresses the root cause?

Accepted Answer

Configure production monitoring to track data drift and model performance metrics, and trigger a retraining and revalidation pipeline when drift or accuracy thresholds are breached.

Answer

Increase the model's temperature parameter in production so it adapts to newer patterns without requiring a retraining cycle.

Answer

Schedule an automatic redeployment of the current model version every 30 days to refresh the runtime instance.

Answer

Pin the model to an earlier version that had higher accuracy and disable future promotions to keep results stable.