← MLOps learning pathMLOps · Module 7 of 10
Monitoring models in production
Detect when models stop working well.
📖 Key ideas
- Monitor system metrics: latency, errors, throughput.
- Monitor data drift: input distributions change.
- Monitor concept drift: relationship between inputs and outcomes changes.
- Track prediction quality when labels arrive.
- Alert and retrain or roll back when needed.
💡 Real-life example: Model monitoring is a regular health check-up; problems caught early are easier to treat.
Data driftInput data distribution changes
Concept driftInput-output relationship changes
Ground truthActual outcome labels
RollbackReverting to a previous model