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Chapter 1. Overview of model registries
Model registry is currently available in Red Hat OpenShift AI 2.16 as a Technology Preview feature. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.
For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.
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 configure the model registry component in OpenShift. For more information, see Configuring the model registry component.
After the model registry component is configured, 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.
To use model registries in OpenShift AI, an OpenShift cluster administrator must configure the model registry component in OpenShift. For more information, see Configuring the model registry component.
After the model registry component is configured, 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.
