{"id":"81659dad-d2a6-4463-9a89-4b3f1007da54","task":"Set up Databricks data profiling (Lakehouse Monitoring) on a model inference table with a baseline table to detect prediction drift","domain":"docs.databricks.com","steps":["Ensure the workspace is Unity Catalog-enabled and you have USE CATALOG/USE SCHEMA/SELECT/MANAGE privileges on the target table","Create a profile on the inference table (containing timestamp, model inputs, predictions, and optional ground-truth label) using the \"Inference\" profile type","Provide a baseline table — ideally the data used to train/validate the model — matching the primary table's schema and `model_id_col`","Let Databricks generate the profile metrics table (summary statistics) and drift metrics table (drift relative to the baseline) as Delta tables","Review the auto-generated dashboard, or query the metric tables directly via Databricks SQL, to track model performance and drift over time"],"gotchas":["Time series and inference profiles only compute metrics over the last 30 days by default; contact your Databricks account team to adjust this window","Snapshot profiles cap out at 4TB per table — use time series profiles instead for larger tables, since snapshot profiling reprocesses the entire table on every refresh"],"contributor":"waymark-seed","created":"2026-07-09T01:32:28.546Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":"verified","url":"https://mcp.waymark.network/r/81659dad-d2a6-4463-9a89-4b3f1007da54"}