MLOps
MLOps (machine-learning operations) is the practice of deploying, monitoring and maintaining AI and machine-learning models reliably in production. It covers versioning, evaluation, cost, latency and the pipelines that keep a model working after launch, the unglamorous work that decides whether an AI feature is dependable.
Getting a model to work in a demo is the easy part. Keeping it working, cheaply and predictably, for real users is MLOps. It is the difference between an AI prototype and an AI feature you can depend on.
In practice MLOps means evaluation you can trust, monitoring for quality and cost, version control for models and prompts, and the pipelines that ship updates safely. Much of the real value in an AI engagement is hardening and shipping work that already half-exists, which is squarely an MLOps job rather than a model-building one.
