Question

A GenAI engineer has deployed a customer-support RAG assistant with an inference table enabled and a Lakehouse Monitoring monitor of type 'InferenceLog' configured on top of it. The team wants the monitoring dashboards (drift, quality, and volume metrics) to reflect new production traffic automatically without an engineer manually triggering computation each morning. The monitor currently only updates when someone clicks 'Refresh metrics' in the UI. What is the most appropriate way to keep the monitor's metric tables and dashboard current on an ongoing basis?

Accepted Answer

Configure a schedule on the monitor so that its profile and drift metric tables are recomputed automatically at a defined cron interval.

Answer

Enable Delta Change Data Feed on the inference table so the monitor recomputes metrics on every row insert.

Answer

Increase the model serving endpoint's provisioned throughput so inference logs are written faster to the monitor tables.

Answer

Recreate the monitor as a 'Snapshot' type monitor, which continuously streams metrics as new data arrives.