Tutorial Overview
The MLflow Model Registry has several core components:
A Centralized Model Store is a single location for your MLflow models, facilitating model versioning, sharing, and deployment in a consistent and efficient manner.
A Set of APIs that allow you to programmatically create, read, update, and delete models.
A GUI that allows you to manually view and manage models in the centralized model store.
The MLflow Model Registry provides some additional functionality that is relevant to model development and deployment:
Model Versioning refers to logging different iterations of a model to facilitate comparison and serving. By default, models are versioned with a monotonically increasing ID, but you can also alias model versions.
Model Aliasing allows you to assign mutable, named references to particular versions of a model, simplifying model deployment.
Model Tagging allows users to label models with custom key-value pairs, facilitating documentation and categorization.
Model Annotations are descriptive notes added to a model.
In this tutorial, you will get up and running with the MLflow model registry in the least amount of steps possible. The topics in this tutorial cover:
Registering a model programmatically to the Model Registry while logging.
Viewing the registered model in the MLflow UI.
Loading a registered model for inference.