AI Model Monitoring and Maintenance

Introduction

AI models degrade over time as data distributions change. Effective monitoring and maintenance are crucial for sustained performance.

Key Monitoring Metrics

• Model accuracy and performance metrics
• Prediction confidence distributions
• Input data drift detection
• Output distribution changes
• System performance (latency, throughput)

Implementing Model Monitoring

import mlflow
from evidently import ColumnMapping
from evidently.report import Report

# Log model metrics
mlflow.log_metric("accuracy", accuracy)
mlflow.log_metric("f1_score", f1)

# Detect data drift
report = Report(metrics=[
    DataDriftPreset(),
])
report.run(reference_data=train_data, 
          current_data=production_data)

    

Maintenance Strategies

• Regular retraining schedules
• A/B testing for model updates
• Gradual rollout of new models
• Automated rollback procedures