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
Practise this in questions
MLOps & ML EngineeringDesign an AI-native observability platform for hundreds of AI products. What's the ingest, storage, and alerting? →hard · PremiumMachine Learning & Data ScienceYour model is live at a customer. What do you monitor, and how do you catch drift before the customer does? →medium · PremiumMLOps & ML EngineeringHow is CI/CD for ML models different from traditional DevOps CI/CD? →easy · FreeSystem Design & Production EngineeringWhat do p50, p95, and p99 latency actually mean, and why do production engineers obsess over the tail? →easy · FreeSystem Design & Production EngineeringIt's 9am Monday and p99 latency is 10x normal. Walk me through your first 30 minutes. →medium · FreeSystem Design & Production EngineeringDesign observability for an LLM application. What do you log, trace, and alert on, and how is it different from normal services? →medium · Free with sign-in