Model Lifecycle

The model lifecycle encompasses all phases of a machine learning model's existence: problem framing and data collection, experimentation and training, evaluation, deployment, monitoring, retraining, and eventual retirement. Managing the full lifecycle requires coordination across data engineering, data science, MLOps, and business stakeholders, along with supporting infrastructure for versioning, reproducibility, and governance.

In enterprise AI programs, lifecycle management is what separates ad-hoc proof-of-concept work from reliable, production-grade systems. Organizations that lack clear lifecycle processes often end up with deployed models of unknown provenance, no retraining cadence, no monitoring, and no defined path for decommissioning. For commerce applications where models directly influence revenue — pricing, search, recommendations — a structured model lifecycle reduces operational risk, accelerates iteration, and provides the audit trail required for regulatory compliance and internal governance.