Model Monitoring & Drift¶

Coming soon.

• Data drift vs. concept drift — different problems with different fixes
• Performance decay: detecting it before a business metric does
• What drift signal should actually trigger a retraining run
• How this differs from AI Evaluation's LLM/agent-specific observability — this topic covers classic ML model monitoring, not hallucination or prompt-quality checks

Related¶

• Model Training Pipelines — the retraining trigger this topic's drift signals feed into
• Model Deployment & Serving — where a monitored model actually runs
• AI Evaluation — Observability — the LLM-specific counterpart to this topic's classic-ML monitoring

Part of MLOps → AI Engineering.