📓Model Registries

Tracking and managing ML models.

Model registries are centralized storage solutions for managing and tracking machine learning models across various stages of development and deployment. They help track the different versions and configurations of each model and enable reproducibility. By storing metadata such as version, configuration, and metrics, model registries help streamline the management of trained models. In ZenML, model registries are Stack Components that allow for the easy retrieval, loading, and deployment of trained models. They also provide information on the pipeline in which the model was trained and how to reproduce it.

Model Registry Concepts and Terminology

ZenML provides a unified abstraction for model registries through which it is possible to handle and manage the concepts of model groups, versions, and stages in a consistent manner regardless of the underlying registry tool or platform being used. The following concepts are useful to be aware of for this abstraction:

RegisteredModel: A logical grouping of models that can be used to track different versions of a model. It holds information about the model, such as its name, description, and tags, and can be created by the user or automatically created by the model registry when a new model is logged.

RegistryModelVersion: A specific version of a model identified by a unique version number or string. It holds information about the model, such as its name, description, tags, and metrics, and a reference to the model artifact logged to the model registry. In ZenML, it also holds a reference to the pipeline name, pipeline run ID, and step name. Each model version is associated with a model registration.