MLOps
Definition pending
We've cataloged "MLOps" but haven't written a full definition yet. Definitions are hand-curated rather than auto-generated, so it takes time to cover every term.
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Practical example
MLOps (Machine Learning Operations) is the discipline of deploying and maintaining ML models in production reliably. It's DevOps + Data Engineering + ML, covering: data pipelines, training automation, model deployment, monitoring, and governance. MLOps matures ML from "works on my laptop" to "works at scale."
Workflow example
MLOps implementation: (1) version everything: data, code, model, hyperparameters, environment, (2) automate the pipeline: data validation → training → evaluation → deployment, (3) monitor: data drift, model performance, system health, (4) governance: access control, audit trails, model registry, (5) for LLMs: MLOps = LLMOps + traditional ML monitoring, (6) maturity levels: 0 (manual) → 1 (automated pipeline) → 2 (CI/CD for ML) → 3 (fully automated with continuous improvement).