Best Practices Intermediate

Deploying AI for traffic engineering in production networks requires careful planning around safety, model monitoring, rollback mechanisms, and integration with existing network infrastructure. These best practices ensure reliable and effective AI-driven traffic management.

Safety and Rollback

A/B Testing Traffic Policies

Model Monitoring

1. Prediction accuracy

   Continuously compare AI predictions (latency, congestion, traffic volume) against actual outcomes. Alert when accuracy drops below threshold.

2. Data drift detection

   Monitor input feature distributions for shifts that might indicate the model is operating outside its training distribution.

3. Performance impact

   Track end-to-end user metrics (latency, packet loss, throughput) and compare AI-managed traffic against baseline.

4. Decision auditing

   Log all AI routing decisions with reasoning for post-incident analysis and compliance requirements.

SDN Integration

Key Metrics