Advanced AI, Model Lifecycle & MLOps — Runbook Template

Purpose and scope

This runbook defines standardized, practical steps and decision criteria for safely deploying, operating, and maintaining machine learning models in production. It covers deployment gates, canary and rollout strategies, monitoring KPIs, drift and bias detection, retraining triggers, rollback procedures, escalation paths, security and data handling, and post-incident review. Use this template as a starting point and adapt thresholds, contacts, and procedures to your system and risk profile.

Quick reference

• Model name / ID: ________________________
• Version / Artifact: ________________________
• Primary owner: ________________________ (Team / Person / Pager)
• Business owner: ________________________
• Criticality: Low / Medium / High / Safety-Critical
• Primary data sources: ________________________
• Deploy window / change freeze: ________________________

Deployment gates checklist

Each gate must be verified before proceeding to the next stage. Record sign-off with initials and timestamp.

1. Artifact & reproducibility
   • Model artifact stored with immutable version ID and provenance recorded.
   • Training code, environment (container image), and random seeds reproducible.
2. Unit & integration tests
   • All unit tests pass.
   • Integration tests for feature pipelines and scoring endpoints pass in staging.
3. Performance validation
   • Evaluation on holdout dataset meets or exceeds acceptance thresholds (precision/recall/AUC/MAE etc.).
   • Calibration and fairness checks performed; documented results attached.
4. Data schema and feature validation
   • Input schema validated; missing or unexpected fields flagged.
   • Feature distribution comparisons with training baseline reviewed.
5. Security & privacy review
   • Secrets, keys and credentials reviewed; no hard-coded secrets in artifact.
   • PII handling, masking and retention policies reviewed and approved.
6. Operational readiness
   • Monitoring, logging, and alerting configured.
   • Runbook steps, rollback criteria and escalation contacts documented.

Canary and rollout plan

Choose one of the following rollout strategies and fill in the parameters. Canary deployments are recommended when possible.

• Canary: route X% of traffic to new model for Y hours/days. Example: 5% traffic for 24 hours, then 25% for 24 hours, then gradual ramp.
• Shadow (non-production scoring): mirror traffic to new model for Z days and compare outputs without serving to customers.
• Blue/Green: deploy new environment and switch traffic after checks pass.
• A/B / controlled experiment: use experiment platform to measure business impact before full rollout.

Canary gating criteria (examples — tailor to your context):

• Model performance (metric M) within ±X% of baseline on live traffic.
• Latency p95 not increased by more than Y ms over baseline.
• No P1 alerts triggered for Z hours.

Rollback criteria and emergency actions

Use these criteria to decide when to pause, rollback, or take emergency action. Always document the reason and timestamp.

• Immediate rollback triggers (examples):
  • Severe degradation of business KPI (e.g., conversion rate drop > 5% within 1 hour attributable to model).
  • Model output distribution shows category collapse or invalid values (e.g., NaNs) for > 1% of requests.
  • Latency spike causing downstream timeouts or SLA breaches (p95 increase > 200ms or as defined by SLA).
  • Security incident or data exposure linked to the deployment.
• Controlled rollback steps:
  1. Notify stakeholders and open an incident (record incident ID).
  2. Pause traffic to the new model (scale down or route traffic away from canary).
  3. Promote previous known-good model artifact or direct traffic to fallback logic.
  4. Run validation checks to confirm system recovered.
  5. Conduct post-incident review (see Post-Incident section).

Monitoring KPIs and example thresholds

Instrument the following signals. Example thresholds are illustrative — adapt them to your operating context.

• Model quality
  • Primary metric (e.g., AUC, precision@k, MAE) — alert when >X% relative drop vs. baseline over rolling 24h.
  • Population-level accuracy or error trends — persistent drift triggers investigation.
• Input & feature monitoring
  • Schema violations count — alert if >0 in production.
  • Feature distribution drift (KL divergence, PSI) — alert when PSI > 0.2 or custom threshold.
  • Missing feature rate — alert if increases by >Y%.
• Latency and throughput
  • p50/p95/p99 latency — alert if p95 increases beyond allowed SLA.
  • Error rate (5xx, timeouts) — alert on sustained spike.
• Business impact
  • Downstream KPI changes (conversion, revenue, false positives) — monitor and correlate with model changes.
• Bias and fairness indicators
  • Group-level metrics (e.g., false positive rate by protected group). Alert on significant divergence vs. baseline.
• Operational health
  • Container/host CPU, memory, disk usage — alert on sustained resource exhaustion.
  • Logging and telemetry health (no missing logs).

Drift detection and retraining triggers

Define both automatic triggers and human-review triggers for retraining.

• Automatic retraining triggers (examples):
  • Input feature PSI > 0.2 for two consecutive weeks.
  • Model metric decay (primary metric down by >5% for 7-day rolling window).
  • Data volume reaching threshold indicating concept shift (e.g., new traffic patterns).
• Scheduled retraining & maintenance:
  • Periodic retrain cadence (weekly/monthly/quarterly) for models expected to drift.
  • Dataset refresh procedures and validation steps before retraining.
• Human review triggers:
  • Significant fairness alerts.
  • Business stakeholder concerns or customer complaints indicating systematic errors.

Security, privacy and data handling checklist

• Ensure minimal data required is collected and used.
• PII is masked, tokenized, or stored separately; access logs enabled.
• Secrets are stored in vaults; no secrets in repo or artifacts.
• Data retention policies are defined and enforced.
• Access controls and least privilege are configured for model artifacts and training data.
• Audit trail enabled for model promotions, configuration changes, and retraining events.

Incident response and escalation

When an alert reaches a threshold or a user reports an issue, follow these steps:

1. Open incident with unique ID and record on-call owner.
2. Perform quick triage: Is the problem reproducible? Scope? Business impact?
3. If immediate rollback criteria met, execute controlled rollback (see Rollback Steps).
4. Notify stakeholders: primary owner, business owner, SRE/infra, legal/compliance (if data exposure suspected).
5. Collect diagnostics: request/response logs, feature distributions, recent deployments, configuration changes, and related traces.
6. Assign root-cause analysis (RCA) owner and schedule post-incident review within T+48 hours.

Escalation contacts (fill in):

• Model owner: __________________ (phone/pager/email)
• On-call SRE: __________________
• Business owner: __________________
• Security contact: __________________
• Legal / Compliance: __________________

Testing & validation pipelines

Automate and require the following checks before promotion:

• Unit tests for pre/post-processing logic.
• Backtest on recent holdout and production-shadow data.
• Regression tests against key slices (e.g., geographic, user segments).
• Fairness checks and explainability summaries for release notes.
• Smoke tests for endpoint readiness and latency under expected load.

Model governance & audit artifacts

• Store model card or release notes containing: intended use, training data summary, evaluation metrics, known limitations, and fairness analysis.
• Record promotion events, who approved them, and the gating evidence.
• Maintain reproducible training pipeline logs and data versioning metadata.

Post-incident review & improvement actions

Each incident should produce a concise RCA documenting:

• Timeline of events.
• Root cause and contributing factors (data, code, infra, process).
• Immediate corrective actions taken.
• Preventive actions and owners with deadlines.
• Follow-up monitoring to verify fixes.

Template checklists and fillable fields

Use these fields to capture the most important operational facts for each deployment.

• Deployment ID: __________________
• Canary start time: __________________
• Canary traffic split: ________%
• Baseline metric value: ________
• Observed metric during canary: ________
• Decision: Promote / Extend Canary / Rollback
• Incident ID (if any): ________

Examples and notes

Example: For a fraud-detection model, use conservative canary thresholds (e.g., alert if false positive rate increases >1 percentage point or if overall conversion dips >2%). For content recommendation, prioritize monitoring business KPIs (time-on-site, CTR) alongside model accuracy.

Adapt monitoring windows and thresholds to traffic volume; low-volume signals require longer observation before decisions.

Where this runbook plugs into your toolchain

Integrate this runbook with CI/CD, model registry, monitoring/dashboards, alerting system, incident management, and data versioning systems. Ensure promotion and rollback operations are auditable and reproducible via automated pipelines where possible.

Change log

Record edits to this runbook (date, author, summary):

• ______________________________________