Model Monitoring & Health Dashboard Spec

Overview

This specification defines the structure, metrics, alerting rules, and operational playbooks for a Model Monitoring & Health Dashboard. The dashboard is intended to help teams detect model degradation quickly, tie model signals to business impact, and provide prescriptive remediation actions so models are not treated as "deploy and forget" artifacts.

Goals (Hungers)

• Detect model performance degradation and data drift early enough to act.
• Surface prescriptive remediation steps and clear ownership for each alert.
• Make model health understandable to technical and business stakeholders.
• Provide audit trails, sample queries, and reproducible checks for governance and compliance.

Audience & Roles

• Model Owner / Product Manager — accountable for business impact and decision to retrain/rollback.
• Data Scientist — investigates model performance, calibration and feature issues.
• Data Engineer — addresses data pipeline problems and sampling issues.
• SRE / Platform — monitors prediction latency and throughput, manages alert routing.
• Compliance / Risk — reviews auditable metrics, drift evidence, and remediation records.

High-level Wireframe

Page layout (top-to-bottom):

1. Header / Snapshot row: model name, version, status badge (OK/Warning/Critical), last evaluation time, owner, business KPI delta.
2. Key cards (single-number metrics with sparklines): Recent AUC (or RMSE), calibration score, drift score (aggregate), prediction volume (24h), median latency.
3. Tabbed detail area: Performance, Data & Features, Inference Health, Business Impact, Alerts & Audit.
4. Right rail or expandable panel: Recommended actions, runbook links, sample queries, and contact routing diagram.

Key Metrics & Widgets

Monitoring Windows, Baselines & Sampling

• Primary evaluation windows: real-time (last 1h), short-term (24h), medium-term (7d), and long-term (30d).
• Baseline: comparison to training performance and to a historical rolling baseline (e.g., previous 30d median).
• Label delay handling: specify expected label arrival times and use delayed-evaluation logic where labels are late.
• Sampling guidance: sample large volumes to manageable size (e.g., reservoir sampling) but preserve rare classes and subgroups for monitoring.

Alerts: Rules, Severity & Examples

Each alert must include: metric, threshold, evaluation window, persistence rule (how many consecutive windows), severity, affected segments, recommended remediation, and owners to notify.

Example Alert Rules

• Performance degradation (major): AUC drops by ≥0.05 vs 7d rolling baseline AND current AUC < 0.70; persist for 2 evaluation windows → Severity: High. Action: Investigate labels, if confirmed rollback to previous model version or promote emergency retrain. Notify: Model Owner, DS, Data Eng, PM.
• Calibration shift (medium): Brier score increases by ≥20% vs baseline for 24h window; persist for 2 windows → Severity: Medium. Action: Recalibration, threshold adjustment, send sample flagged cases to DS for review.
• Feature drift (medium/high): Any feature PSI > 0.25 OR KS statistic p-value < 0.01 for a group of critical features; persist for 24h → Severity depends on impacted features. Action: Inspect upstream data pipelines, validate incoming schema, check for categorical level changes, and run backfill checks.
• Prediction volume anomaly (low/medium): sudden drop or spike > 3σ from baseline; persist 1-2 windows → Action: Check upstream service availability and API contract changes. Notify SRE and Data Eng.
• Latency breach (high): p95 latency > SLA (e.g., 500ms) for 15 minutes → Severity: High. Action: Circuit-break or route to fallback. Notify SRE and Model Owner.

Recommended Mitigations & Playbooks

• Minor drift/performance change: collect labeled samples, perform root-cause analysis, monitor whether change stabilizes before retraining.
• Persistent drift or performance failure: run incremental retrain using recent data with guardrails; validate against holdout; perform shadow testing and manual review before promotion.
• Data pipeline issue: rollback to previous stable data snapshot or pause scoring while pipeline is fixed; run data reconciliation checks.
• Calibration issues: apply post-hoc calibration (Platt scaling / isotonic) or adjust decision thresholds while investigating feature shifts.
• Critical outages: switch to safe default logic or fall back to human-in-the-loop processes if available.

Ownership, Notifications & Routing Diagram

Every alert type should map to a notification flow (email, Slack, PagerDuty) and an owner role. Example routing:

• High-severity performance or latency alerts: immediate page to on-call SRE and Model Owner; follow-up ticket to Data Engineering and DS teams.
• Medium-severity drift or calibration: Slack to DS channel + email to Model Owner; create incident with 24–48h SLA for triage.
• Low-severity volumetric anomalies: email digest to Data Eng and Product for next-business-day review.

Include a simple diagram in the right rail showing arrows from Dashboard → Alerting System → Contact Roles (Model Owner, DS, Data Eng, SRE) → Runbook Links.

Example Queries & Computation Templates

Provide canonical SQL/analytic examples for implementers. These should be adapted to your data model and naming conventions.

<!-- Example: compute daily AUC (classification) -->
SELECT
  DATE(evaluation_time) AS day,
  auc_score(predicted_label, true_label) AS daily_auc
FROM predictions
WHERE evaluation_time >= current_date - interval '30' day
GROUP BY day
ORDER BY day;

<!-- Example: PSI for a numeric feature between baseline and current -->
-- bucket baseline and current, compute PSI per bucket, sum

Include ready-to-run snippets for your platform (SQL, Spark, dbt, BigQuery, Snowflake) and tests validating that the metric computation matches training-time definitions.

Data Requirements & Lineage

• Specify required inputs: feature table names, data types, expected ranges, schema contract, and sample size expectations.
• Label sources: clearly document label generation process, expected delay, and confidence in label quality.
• Lineage: link each metric to the source pipeline and job that produces it so the dashboard can provide “why” and “where” for any signal.

Auditability & Governance

• Retention of metric history and raw samples for at least the compliance-required window (e.g., 1 year).
• Record of model version, training dataset snapshot, and evaluation artifacts for every alert and remediation event.
• Support for exporting evidence bundles (metric snapshots + example records) for compliance reviews.

Visualization Guidance & UX Notes

• Prefer small multiple charts and sparklines for trend context; use overlays to compare current vs baseline.
• Use color and severity consistently (green/yellow/red) but ensure accessible contrast and alternative text for audit export.
• Allow filtering by time window and subgroup (segment, geography) and provide quick drilldowns to raw records for sampled examples.

Implementation Checklist

1. Confirm metric definitions and evaluation windows with DS and Product.
2. Implement metric jobs & tests; store outputs in analytics schema with stable names.
3. Wire dashboard widgets to metric outputs; implement sampling and retention policies.
4. Configure alert rules and notification routing; run simulated alert tests (attack-and-fail tests).
5. Publish runbooks and assign owners; schedule periodic review cadence (weekly for critical models).

Notes & Caveats

Thresholds and alert persistence rules are context-dependent. Use conservative thresholds for low-risk models and stricter thresholds for high-risk or regulated use-cases. Always validate monitoring signals against labeled data before enacting destructive remediation (e.g., automated rollback).

Appendix: Quick Reference Threshold Examples

• AUC drop >= 0.05 vs baseline → investigate
• PSI > 0.25 for critical features → investigate
• Brier score increase > 20% → consider recalibration
• Prediction volume change > 3σ → check upstream changes
• p95 latency > SLA → page SRE