What MLOps means in practice
MLOps is to ML what DevOps is to software: the discipline of moving models from notebook to production reliably and repeatably. Versioning of data, models and code; reproducible training; automated deployment; monitoring of drift, latency and accuracy; rollback when something regresses; lineage from inference back to training data. For generative AI the discipline picks up additional concerns — prompt versioning, evaluation against an SME-built test set on every change, A/B testing of prompt or model variants. The point is not the tooling (Weights & Biases, MLflow, Langfuse, Kubeflow); it is the operational maturity that lets a team upgrade a model on a Tuesday afternoon without a Friday-night incident.
Related terms
Evaluation (evals) →
Systematic testing of an AI system against a curated set of inputs to measure quality on the dimensions the business cares about — accuracy, faithfulness, safety, format.
Drift (data + model) →
The phenomenon where a model's input distribution or its accuracy degrades over time as the world it predicts about changes.
LLM observability →
Capturing every LLM call (prompt, retrieved context, response, latency, cost, user feedback) in a structured store so production behaviour can be inspected and improved.
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.