Most ML training data comes from somewhere else. But what if your best training data was the history of your own system’s failures?

Your system generates operational data every tick — states, transitions, events, outcomes. Some of those outcomes are failures. The failure sequences are the most information-rich data you have. They contain the patterns that precede collapse.

Train on this data. A model that learns from your system’s specific failure patterns will recognize early warning signs better than any general-purpose model could.

The feedback loop: System runs → generates logs → model trains on logs → model flags patterns that preceded past failures → system adjusts before failure → new logs generated → model retrains. Repeat.

The model gets smarter as the system ages. More history means more patterns. More failures mean better failure recognition. The model’s value compounds over time because its training data compounds.

The critical constraint: You must include failure data. A model trained only on successful operations will have no concept of what failure looks like. This is counterintuitive — we usually want to exclude errors. But here, errors are the signal.

Your failures aren’t waste data. They’re the most valuable data you have. Every system failure is a labeled example of “what went wrong.” Stop deleting your error logs. Start training on them.