Drift Monitoring Pipeline Engineer

Senior MLOps drift detection specialist that designs production-grade monitoring pipelines with statistical tests, alerting thresholds, and automated retraining triggers.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

83.75

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.3

Completeness (10%)

8.3

User Satisfaction (20%)

8.2

Decision Usefulness (15%)

8.4

Best for

▸Building PSI-based data drift detection for production credit scoring models
▸Implementing concept drift alerts for fraud detection systems with automated retraining triggers
▸Designing streaming drift monitors for healthcare ML models with regulatory compliance requirements
▸Creating feature-level drift tracking pipelines for e-commerce recommendation engines
▸Setting up statistical drift thresholds and escalation workflows for high-stakes financial models

What you'll get

●Detailed monitoring architecture diagram with specific statistical tests (KS, PSI, MMD) mapped to feature types, threshold recommendations, and alerting escalation matrix
●Production-ready Python code using Evidently/NannyML with custom threshold logic, automated retraining triggers, and integration with MLflow Model Registry
●Statistical drift detection strategy document with baseline establishment procedures, reference window selection logic, and regulatory compliance considerations

Not designed for ↓

×Initial model training or feature engineering (focuses on post-deployment monitoring)
×Model performance debugging or accuracy improvements (detects when to retrain, not how to improve)
×Data quality validation during ETL processes (monitors distribution changes, not data corruption)
×Business metrics monitoring or KPI dashboards (tracks statistical drift, not business outcomes)

Expects

Production ML model details including feature schemas, training data baselines, business risk tolerance, and infrastructure constraints for implementing monitoring pipelines.

Returns

Complete drift monitoring architecture with statistical test selections, alerting thresholds, automated response triggers, and implementation code for production deployment.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

drift-detectionmlopsmodel-monitoringdata-driftconcept-driftpsistatistical-testingretrainingalertingevidentlynannymlproduction-ml

Research Foundation: 7 sources (4 official docs, 2 academic, 1 industry frameworks)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/16/2026

Initial release

Prerequisites

Use these skills first for best results.

Supervised Learning EngineerGold

Works well with

Feature Store ArchitectGold ML Experiment TrackerGold MLOps Platform EngineerGold Model Deployment OptimizerPlatinum Observability Pipeline DesignerGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Anomaly Detection SpecialistGold MLflow Experiment TrackerGold Real-Time Feature Serving ExpertGold

Common Workflows

MLOps Production Monitoring Pipeline

Deploy model to production, implement drift monitoring with automated alerts, and track retraining experiments when drift is detected

Model Deployment Optimizer→drift-monitoring-pipeline-engineer→MLflow Experiment Tracker

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