Technical
Model-development and model-usage pipeline
Defined ML lifecycle stages from data to operation.
Definition
The pipeline of stages identified for ML systems — including data acquisition and ingestion, data preparation and labelling, modelling (selection, training, tuning), verification and validation, deployment and operation (monitoring, logging, updates) — specifying where evaluation metrics and governance controls apply.
Related Terms
ML pipeline (lifecycle stages)
Defined stages for model development, validation and operation....
Model lifecycle stages
Phases of model development: training, validation, deployment....
AI system lifecycle
Stages of AI development from data to monitoring....
Model lifecycle
Phases from design and data curation to decommissioning....
AI system life cycle
Phased lifecycle from design to decommissioning for AI governance....