Drift Detection
Detect when production data or model performance degrades.
Maturity level: L3 — Can Deploy
Six perspectives on Drift Detection
Roadmap
Learn after model serving and observability are in place.
Architecture
Feedback loop from production metrics to retraining.
Company
Expected in mature MLOps and LLMOps teams.
Projects
Add drift checks to every deployed model.
Interview
Monitoring strategy and incident response.
Career
Production ML ownership.
What & Why
What: Monitoring statistical shifts in input data and model predictions over time.
Why: Models silently fail when the world changes — drift detection triggers retraining.
Build this
Monitor a deployed model and alert when feature distributions shift.
Production reality
- ! Alert fatigue
- ! False positives
- ! Delayed detection
- ! Missing baselines
Interview preparation
- Types of drift
- How would you monitor an LLM in production?
Connected skills
Explore Drift Detection in the interactive universe or train with live cohorts.