`mlflow models serve`

The simplest possible production

mlflow models serve -m 'models:/credit-scoring/Production' -p 5000

Under the hood MLflow spins up a Flask/FastAPI server that:

• loads the model in its pyfunc flavor,
• restores the recorded environment (python_env.yaml),
• enforces the signature on inbound payloads,
• exposes /invocations (and /ping).

This is fine for staging and small-scale traffic. For real production you'll wrap it (Docker), scale it (KServe / SageMaker / Azure ML) and put it behind an API gateway — but the loaded model is identical to the one you served locally.

mlflow models serve is the food truck of model serving. Same chef, same recipe, parked on the kerb. Once the queue gets long enough you graduate to a restaurant (Docker + KServe), but the menu doesn't change.