MLOps (Machine Learning Operations) represents the tools and practices used to deploy, test, monitor, and automate the lifecycle of AI models in production envi
MLOps applies DevOps discipline to machine learning systems: versioned data, models, and prompts; automated training/evaluation pipelines; staged deployment with rollback; and production monitoring for performance, drift, and cost. The 2026 extension, LLMOps, adds prompt registries, eval-gated releases, trace observability for agents, and token-spend governance across providers.
Models are easy to demo and hard to operate: without MLOps, organizations accumulate unmonitored, unreproducible AI that fails silently. MLOps is the difference between one fragile pilot and a portfolio of AI features shipping safely every week, and it's where AI cost control actually lives.
The unit of change shifts from retrained models to prompts, contexts, and provider versions, so versioning covers prompts, evaluation replaces accuracy metrics with task-specific eval suites, and observability means tracing multi-step agent behavior plus token spend.
At the second model or first production incident: whichever comes first. Even single-feature teams need eval gates, monitoring, and rollback; the tooling can be lightweight, the discipline can't.
Quality (eval scores, faithfulness), reliability (latency, error rates), economics (token cost per task), and drift signals on inputs and outputs: each tied to alerts with owners, not just dashboards.