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mlops

/ˈɛmɛlɒps/ - n.

1 [colloq.] The operating discipline a model needs, requested only after the model is already in production.Keep. Punchy.This is the problem.

Working definition

2. The discipline of versioning, deploying, monitoring, and retraining models with reproducible, owned pipelines.

Evidence

See also

ai maturityTerm struck: cannot be measured until the field defines the quality, ownership, and monitoring the score is meant to rate.
inferenceThe part priced per call, unlike the definitions it relies on, which are free and missing.
model monitoringA panel that shows the model is healthy by reporting every signal except whether it is still right.
model registryA list of every model in production except the three a team is quietly running from a notebook.