Processes in this Phase

7.1 →

Performance Monitoring

Continuously track model performance in production using dashboards, alerts, and KPIs. Ensure the system meets SLAs and business objectives.

Dashboards Alerting SLA Tracking
7.2 →

Drift Detection

Monitor for data drift and concept drift that can degrade model performance over time. Implement statistical tests and automated detection systems.

Data Drift Concept Drift Statistical Tests
7.3 →

Model Retraining

Establish triggers and pipelines for retraining models with new data. Maintain model freshness while ensuring quality and stability.

Retraining Pipeline Triggers Automation
7.4 →

Continuous Improvement

Iterate on the AI system based on feedback, new requirements, and learnings. Plan enhancements and maintain the improvement roadmap.

Feedback Loop A/B Testing Roadmap

🔄 The Continuous Cycle

Phase 7 feeds back into earlier phases when significant changes are needed. Drift detection may trigger return to Phase 3 (Data Preparation), new business requirements may restart from Phase 1, and model improvements cycle through Phases 4-6.

← Back to Phase 1 ← Back to Phase 3 🔄 View Full Flow