With the model registry, you can store your ML models in a central location and collaboratively manage their lifecycle. This enables stakeholders and applications to access your models for various purposes, such as audits, tests, or stage management.
You can use Neptune to manage your models in the following ways:
Create models and track generic model metadata, such as the model signature and validation dataset.
Create versions of your models:
Log parameters and other metadata that might change from one version to another.
Track or store model binaries.
Track the performance of specific model versions.
Manage model stage transitions using four available stages.
Query and download any stored model files and metadata.
How it works
Instead of a Run object, you initialize and work with a Model object. This puts the metadata in the Models tab of your project.
Then, for each model, you create versions as you refine the model, using Model version objects.
In the example screenshot below, you can see five logged versions of a pre-trained classification model CLS-PRE.