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 / continuous-integration-ml
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.
What this means in practice
Prevents regressions in model code and training-data interfaces before they reach pipeline runs.
Synonyms
CI for ML , ML CI , ML continuous integration
See also
- 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).
- Pipeline — An automated execution graph connecting data ingestion, feature engineering, training, evaluation, and deployment stages — parameterized, versioned, and re-runnable.
- Data leakage — Information from the test or validation set inadvertently entering training — through preprocessing, feature engineering, target encoding, or time-ordered splits — inflating offline metrics and producing over-optimistic ship decisions.
- 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.