Governance
ML pipeline (lifecycle stages)
Defined stages for model development, validation and operation.
Definition
The sequence of lifecycle stages identified for ML systems — including data acquisition and ingestion, data preparation and labelling, modelling (selection, training, tuning), verification and validation, and deployment and operation — used to structure evaluation, monitoring, documentation and governance activities.
Related Terms
Model-development and model-usage pipeline
Defined ML lifecycle stages from data to operation....
AI system lifecycle
Stages of AI development from data to monitoring....
AI system life cycle
Phased lifecycle from design to decommissioning for AI governance....
Model lifecycle stages
Phases of model development: training, validation, deployment....
Life cycle
The sequence of phases governing an AI system from design to decommissioning....