The COMPEL Glossary Graph visualizes relationships between framework terminology, showing how concepts interconnect across domains, stages, and pillars. Term nodes cluster by pillar affiliation while cross-references reveal semantic dependencies — for example, how risk appetite connects to control effectiveness, model governance, and assurance requirements. This network representation helps practitioners navigate the framework vocabulary and understand that COMPEL terminology forms a coherent conceptual system rather than isolated definitions.
COMPEL Glossary / pipeline
Pipeline
An automated execution graph connecting data ingestion, feature engineering, training, evaluation, and deployment stages — parameterized, versioned, and re-runnable.
What this means in practice
Pipelines convert one-off experiments into reproducible, auditable production workflows; pipeline-as-code is the MLOps equivalent of infrastructure-as-code.
Synonyms
ML pipeline , training pipeline , inference pipeline
See also
- Continuous integration (ML) — Automated test and build of model code, data contracts, and training scripts on every change — extended from software CI with data-schema validation, model-schema validation, and lightweight training smoke tests.
- Continuous delivery (ML) — Automated, governed promotion of models through lifecycle stages — development, staging, production — with gated checkpoints (evaluation thresholds, bias checks, cost thresholds, human approval where required).
- Experiment tracking — The infrastructure and practice of recording artifacts, metrics, parameters, environment, and lineage for every experiment run — enabling later reproduction, comparison across runs, and audit.