FREE LESSON · AI & machine learning · 4 OF 4
MLOps: Model Deployment, Drift and Feedback
ML mastery: the model lives inside a system — Deployment, drift, feedback, safety, and human judgment
Deployment changes the data-generating process.
The model influences decisions, which influence future labels and who appears in the data. Feature pipelines can skew between training and serving. Populations, incentives, and measurement systems drift. Production therefore needs input validation, shadow or staged rollout, outcome monitoring, fallback behaviour, auditability, and retraining criteria.
A model artifact can be frozen while the system around it becomes wrong.
Monitor mechanisms, not one score
Track schema and feature health, input distribution, prediction distribution, latency, decision rate, delayed outcomes, calibration, subgroup performance, and business guardrails. A shifted input is a warning, not proof of harm; unchanged inputs do not prove outcomes are sound. Link alerts to investigation and rollback actions.
Monitoring is a map from observable changes to a response, not a wall of charts.