Checklist

AI Ops & Monitoring — Checklist

An actionable, savable checklist for verifying model and pipeline observability, drift detection, performance thresholds, user feedback, retraining triggers, and incident playbooks. Fill the form to create a stored record you can use for audits, follow-ups, or to trigger next steps.

Interactive Tool

AI Ops & Monitoring — Checklist

Use this checklist to verify core AI monitoring, drift detection, observability, and incident response practices for a deployed model or hybrid workflow. It preserves the essentials: data drift signals, performance degradation thresholds, user feedback channels, retraining triggers, and an incident response template. Complete fields, save the result, and schedule follow-ups.

Who completed this checklist.
YYYY-MM-DD
Tick items that are present and validated.
Automated checks against input distributions or features.
Applicable when labels arrive with delay.
Alerts for incomplete or delayed data.
List the metrics (e.g., accuracy, F1, AUC, MAPE, business KPI).
Record baseline values and the thresholds that should trigger an alert or investigation.
Percentage drop from baseline that should trigger action. Enter numeric percent (e.g., 5).
Desired maximum median latency in milliseconds.
Acceptable failure/error rate percentage.
E.g., Prometheus, Grafana, Sentry, DataDog, custom logs.
Playbook should include detection, rollback, communication, and roles.
Where the team can find the playbook.
List primary contacts (team, role, paging instructions).
Includes root cause analysis, corrective actions, and owners.
How retraining is normally triggered.
Includes data validation, training, testing, and deployment steps.
Reduces risk when releasing model changes.
List revenue, conversion, CTR, process throughput, or other KPIs the model impacts.
How feedback is collected and routed to the ML team.
1 = low, 5 = high
YYYY-MM-DD
Summary of gaps found and recommended next steps.
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