Core

Model Monitoring

Model monitoring is watching a deployed model's health the way you watch a service: prediction distributions, input drift, latency, error and abstain rates, and the business metric the model is supposed to move. The skill interviewers test is triage: telling a model problem apart from a data or pipeline problem, and knowing which signal fires first.

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RELATED CONCEPTS

Data and Concept Drift Observability for AI Systems CI/CD for Models A/B, Canary and Shadow Testing

LESSONS THAT TEACH THIS

Foundations of Forward Deployed EngineeringConnect operational signals to outcomes and expose missing evidence30 min→Foundations of Forward Deployed EngineeringDefine service levels with explicit denominators and a usable response policy30 min→Foundations of Forward Deployed EngineeringTransfer operational capability with evidence, rehearsal and accepted ownership30 min→

PRACTICE THIS IN REAL QUESTIONS

MLOps & ML EngineeringHow is CI/CD for ML models different from traditional DevOps CI/CD?→System Design & Production EngineeringWhat do p50, p95, and p99 latency actually mean, and why do production engineers obsess over the tail?→System Design & Production EngineeringIt's 9am Monday and p99 latency is 10x normal. Walk me through your first 30 minutes.→ML System Design (Product)Build a fraud-detection model for payments.→System Design & Production EngineeringDesign observability for an LLM application. What do you log, trace, and alert on, and how is it different from normal services?→MLOps & ML EngineeringDesign an automated retraining pipeline. What should trigger retraining?→