The 4-Stage AI Asset Lifecycle: How to Manage Your Models, Datasets, and Labels Without Losing Track

AI teams routinely manage code with Git but lack systematic approaches for the three core ML artifacts: labeled datasets, trained models, and label schemas. A 4-stage lifecycle framework (Create, Version, Deploy, Retire) addresses this gap. The Create stage emphasizes provenance metadata and quality baselines. Versioning must distinguish additive, corrective, and schema changes — something standard Git cannot handle. Deployment records must link model artifacts to exact dataset and schema versions for debugging and compliance. Retirement means archival with context, not deletion. Tools like DVC, MLflow, Weights & Biases, and LakeFS cover parts of this workflow, but label schema versioning often requires custom solutions. The EU AI Act (enforcement began 2025) mandates traceability for high-risk AI systems, making lifecycle management a legal requirement. A minimum viable implementation requires only structured directories, changelogs, and experiment logs — no enterprise tooling needed on day one.