modelbank

Modelbank is a centralized repository designed to store, preserve, and facilitate the reuse of trained machine learning models and related artifacts. It typically houses models, training scripts, data schemas, evaluation results, and provenance metadata. The goal is to enable discovery, collaboration, reproducibility, and safe deployment across teams and organizations.

Core features include model versioning and lineage tracking, standardized metadata and model cards, licensing and access controls, evaluation benchmarks, and reproducibility hooks that capture experimental conditions. A modelbank provides search and discovery capabilities, supports portable packaging, and offers APIs for integration with training pipelines, deployment systems, and monitoring tools.

Common use cases include collaborative research, open science, and enterprise governance. In research contexts, modelbanks facilitate sharing and reuse of pre-trained models and baselines. For production teams, they support reproducible deployments, audit trails, and policy-driven access control.

Modelbanks relate to model hubs such as public model repositories and to MLOps model registries and artifact stores. They emphasize provenance, licensing, and governance to promote reuse while minimizing risk from data leakage or incompatible licenses.

Security, privacy, and ethics are central considerations. Addressed topics include licensing compatibility, data provenance, access control, secure serving, and compliance with regulations. While there is no universal standard, communities pursue interoperable schemas and open formats to improve long-term preservation and collaboration.