ML Model Monitoring & Observability Platform
Production ML model performance tracking and drift detection
Technology Stack
Overview
Built a comprehensive ML model monitoring platform to track model performance, detect data drift, and ensure production model reliability across 20+ machine learning models.
Business Problem
Production ML models were degrading silently, leading to poor business outcomes. The data science team had no visibility into model performance drift, data quality issues, or prediction accuracy in production environments.
Approach & Solution
Implemented end-to-end monitoring using MLflow for experiment tracking, custom drift detection algorithms, automated retraining pipelines, and comprehensive alerting systems. Created standardized model deployment patterns with built-in observability.
Challenges Overcome
Detecting subtle model drift in high-dimensional feature spaces, balancing monitoring frequency with computational costs, creating meaningful alerting that reduces false positives, and implementing automated retraining without human intervention.
Results & Impact
Reduced model performance degradation incidents by 80% and improved model accuracy maintenance. Automated detection and remediation saved 15 hours per week of manual model monitoring across the data science team.
Shows expertise in MLOps, production ML systems, automated monitoring, and building reliable AI infrastructure that scales across multiple business units.
Key Highlights
Quick bullets for recruiters and hiring managers:
- 80% reduction in model performance incidents
- Automated monitoring for 20+ production models
- 15 hours/week saved in manual monitoring effort
- Real-time drift detection and automated alerts
- Comprehensive model performance dashboards