Monitoring & Observability

Monitoring an AI system in production is fundamentally different from monitoring traditional software. A web application either works or it doesn't - it returns errors, times out, or crashes in observable ways. An AI model can fail silently, returning plausible-looking outputs that are subtly wrong. Catching these failures requires monitoring at multiple levels. Infrastructure monitoring covers the basics: is the serving infrastructure healthy, are requests being processed, what are the latency and error rates? Model performance monitoring tracks whether the model's predictions are still accurate, comparing them against ground truth where available or using proxy metrics where it isn't. Data monitoring watches for changes in the input data - shifts in distribution, unexpected values, missing fields - that might indicate the model is being asked to handle situations it wasn't trained for. Observability goes beyond monitoring by helping you understand why something went wrong, not just that something went wrong. This means logging inputs and outputs, tracking feature values, and maintaining enough context to diagnose issues. Tools like Evidently, Fiddler, Arthur AI, and WhyLabs specialise in ML monitoring. The investment in monitoring pays off most when things go wrong - and with AI systems, things will go wrong eventually. The question is whether you catch it in minutes or months.