modelinventory

Model inventory is a centralized catalog that stores information about machine learning models, their versions, artifacts, and related metadata. It supports discovery, reuse, deployment, and governance by providing a single source of truth for models across an organization. A well-maintained model inventory helps teams track provenance, compare alternatives, and ensure reproducibility.

Key components typically included are model metadata (name, version, authors, license), lineage (data sources, training code, dependencies), training parameters and datasets, performance metrics, evaluation results, and deployment status. Artifacts such as trained model binaries, container images, inference pipelines, and supporting code may also be stored or linked. Access controls, audit trails, and compliance information are often incorporated to support governance.

Lifecycle and governance aspects focus on registration, validation, promotion through stages (e.g., development, staging, production), monitoring, and retirement. A model inventory often integrates with an MLOps stack to automate versioning, testing, and deployment, and may include model cards or documentation to communicate risk, intended use, and limitations to stakeholders.

Common tools and platforms provide model registries and inventory capabilities, including MLflow, Kubeflow, DVC, Weights & Biases, and cloud offerings such as AWS SageMaker Model Registry, Azure ML, and Google Vertex AI. These systems facilitate metadata schemas, lineage capture, and policy enforcement across teams.

Benefits of a model inventory include improved traceability, faster model reuse, standardized governance, and better risk management. Challenges can involve metadata completeness, drift monitoring, security, and maintaining consistency across disparate systems. Best practices emphasize standardized schemas, automated metadata capture, semantic versioning, and regular audits.