Model Monitoring in Production Explained

Why deployed models need monitoring: drift detection, performance tracking, alerting and closing the feedback loop.

▶ Open the simulation

Best Practices

Checklist

Monitor business and ML metrics
Drift tests with thresholds and windows
Alert deduplication and suppression
On-call rotation and response playbooks
Audit trail of incidents and fixes

Anti-Patterns

Alert storms without deduplication
Only accuracy; no calibration or business KPIs
No ownership or escalation
Ignoring privacy and PII governance
No tests before production

Worked Examples

Batch Monitoring

# ETL → compute metrics → drift tests → alerts

Real-Time Monitoring

# Stream telemetry → online tests → alerts → playbooks

The Math Behind It

Tests

KS for continuous, Chi-square for categorical
PSI for distribution shifts

Confidence

Use intervals and power analysis to set thresholds and sample sizes.

Frequently Asked Questions

When to stop tuning thresholds?

Stable false-positive rates.

How to share?

Dashboards and incident reports.

How to reproduce?

Version telemetry schemas.

How to secure?

Access controls and anonymization.

How to govern?

Approvals and audits.

Tracking?

Dashboards and ticketing integration.

Deployment?

K8s jobs and streaming systems.