Chapter 1. Overview of model registries

A model registry is an important component in the lifecycle of an artificial intelligence/machine learning (AI/ML) model, and a vital part of any machine learning operations (MLOps) platform or ML workflow. A model registry acts as a central repository, holding metadata related to machine learning models from inception to deployment. This metadata ranges from high-level information like the deployment environment and project origins, to intricate details like training hyperparameters, performance metrics, and deployment events. A model registry acts as a bridge between model experimentation and serving, offering a secure, collaborative metadata store interface for stakeholders of the ML lifecycle.

Model registries provide a structured and organized way to store, share, version, deploy, and track models.

To use model registries in OpenShift AI, an OpenShift cluster administrator must enable the model registry component. For more information, see Enabling the model registry component.

After the model registry component is enabled, an OpenShift AI administrator can create model registries in OpenShift AI and grant model registry access to the data scientists that will work with them. For more information, see Managing model registries.

Data scientists with access to a model registry can store, share, version, deploy, and track models using the model registry feature. For more information, see Working with model registries.

With the addition of the model catalog feature, you can discover models that are available and ready for your organization to register, deploy, and customize. This functionality starts with connecting users with the Granite family of models, as well as the teacher and judge models used with the LAB-tuning workflow.

For more information about using the LAB-tuning workflow, see Customizing models with LAB-tuning.