Core
Observability for AI Systems
You cannot operate what you cannot see, and an AI system has failure modes a normal service does not: the prompt, the retrieved context, the model output, and the slow drift in quality over time. Observability for AI means logging and tracing every stage of the chain with a shared request ID, so when an answer is wrong you can reconstruct exactly why.
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Practise this in questions
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? →medium · Free with sign-inMLOps & ML EngineeringHow do you monitor a model in production when ground-truth labels arrive late, or never? →hard · Free with sign-inMachine 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 EngineeringBuild a model-observability platform that a dozen teams self-serve. What's the contract, and what does the platform own? →hard · PremiumMLOps & ML EngineeringDesign an AI-native observability platform for hundreds of AI products. What's the ingest, storage, and alerting? →hard · PremiumSystem Design & Production EngineeringDesign tracing for a multi-step agent where the failures are silent. The run returns a plausible answer that is wrong. →hard · Premium