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Using the model registry

The model registry enables you to manage models separately from deployments.

In a typical Modelbit deployment, models are pickled with the code of the deployment and both are versioned together.

In some cases it's preferable to version the model separately from the code of the deployment, to use the same model in multiple deployments, or to load different models dynamically based on runtime parameters. The model registry is designed for these use cases.

Using the registry starts requires only two lines of code: mb.add_model to add a model to the registry, and later mb.get_model to retrieve that model back into your Python environment.

Read on:

  1. Getting started with the model registry
  2. Deployments using the registry
  3. Training jobs saving to the registry
  4. Working with model metrics