AI ops is the operational discipline of running AI systems in production. It covers deployment, monitoring, evaluation, cost management, drift detection, model rotation, and the lifecycle management of prompts, models, and orchestration logic.
AI ops is to AI systems what DevOps is to software systems. The deliverable is reliability and observability — the team can answer what the system did, why it did it, and whether it is still performing within expected bounds.
The discipline is young. Tooling is fragmented. Most teams running AI in production are inventing significant parts of their own AI ops infrastructure.
Why AI ops Matters for Distributed Teams
The teams that run AI in production sustainably invest in AI ops early. The teams that skip it discover, eventually and expensively, that no one knows why the system is doing what it is doing.
Frequently Asked Questions
What is AI ops?
AI ops is the operational discipline of running AI systems in production. It covers deployment, monitoring, evaluation, cost management, and lifecycle management of prompts, models, and orchestration logic. It is to AI what DevOps is to software.
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