ML Model Monitoring & Drift Detection Pipeline

Status

🚧 In Progress — building an end-to-end monitoring workflow for detecting data drift and concept drift in deployed ML systems, designed with auditability in mind.

Overview

This project focuses on practical MLOps monitoring patterns:

• Data drift detection (feature distribution changes)
• Concept drift detection (performance degradation over time)
• Alerting + reporting for model governance and lifecycle management

Target Capabilities

• Batch monitoring pipeline (daily/weekly)
• Drift metrics (PSI, KS test, KL divergence where applicable)
• Performance tracking + thresholds
• Alerting hooks (email/Slack-ready structure)
• Audit-friendly reporting artifacts

Tech Stack (Target)

• Python
• Pandas / NumPy
• Scikit-learn
• Lightweight storage (CSV/SQLite/Postgres)
• Optional: Docker + Cloud scheduler/runtime

Milestones

• Create baseline dataset + reference distributions
• Implement drift metrics (PSI + KS as baseline)
• Build report generation (CSV/JSON + simple plots)
• Add alert thresholds and alert payload structure
• Add model performance tracking (accuracy/F1/MAE depending on task)
• Package pipeline for scheduled execution
• Document production considerations (versioning, audit logs)

Production Considerations

Designed to support regulated environments where monitoring outputs must be:

• reproducible
• reviewable
• timestamped
• traceable to data versions and model versions