Monitoring Overview

The ML Monitoring module provides continuous tracking of model performance, data drift detection, and automated retraining triggers for models deployed in production. It ensures that model quality remains high over time by detecting degradation early and initiating corrective actions.

Monitoring Architecture

Production Traffic --> Prediction Logger --> Monitoring Service
                                                |
                              +-----------------+-----------------+
                              |                 |                 |
                       Drift Detection   Performance Monitor   Retraining Trigger
                              |                 |                 |
                         Alert System      Dashboard         ML Pipeline

Key Components

Component	Location	Purpose
Drift Detection Service	`src/monitoring/drift_detection_service.py`	Multi-dimensional drift analysis
Performance Monitoring	`src/monitoring/performance_monitoring_service.py`	Model accuracy and latency tracking
Model Monitoring Service	`src/monitoring/model_monitoring_service.py`	Overall monitoring orchestration
Retraining Triggers	`src/monitoring/retraining_trigger_service.py`	Automated retraining logic

Monitoring Dimensions

Dimension	Metrics	Frequency
Data drift	Feature distribution shifts	Hourly
Concept drift	Prediction quality degradation	Hourly
Performance	Accuracy, F1, latency, throughput	Real-time
Resource	CPU, memory, GPU utilization	Real-time
Traffic	Request volume, error rates	Real-time

Alert Severity Levels

Severity	Response	Example
`info`	Log only	Minor feature drift below threshold
`warning`	Notify team	Moderate drift detected, monitoring closely
`critical`	Page on-call	Significant accuracy degradation
`emergency`	Auto-remediate	Model serving errors above 5%

API Endpoints

Endpoint	Method	Purpose
`/api/v1/monitoring/drift/:model_id`	GET	Get drift status for a model
`/api/v1/monitoring/performance/:model_id`	GET	Get performance metrics
`/api/v1/monitoring/alerts`	GET	List active monitoring alerts
`/api/v1/monitoring/config/:model_id`	PUT	Configure monitoring thresholds
`/api/v1/monitoring/retrain/:model_id`	POST	Trigger manual retraining

Configuration

Environment Variable	Default	Description
`MONITORING_ENABLED`	`true`	Enable monitoring module
`MONITORING_INTERVAL_SECONDS`	`3600`	Default monitoring check interval
`MONITORING_RETENTION_DAYS`	`90`	Monitoring data retention
`MONITORING_ALERT_COOLDOWN`	`3600`	Minimum seconds between duplicate alerts

Detailed Sections

Section	Content
Drift Detection	Feature drift, concept drift, statistical tests
Performance Monitoring	Accuracy tracking, latency, throughput
Retraining Triggers	Automated and rule-based retraining

Monitoring & Drift Drift Detection