Register a Model

To register a model implies pushing the model into the Pipeline environment where it can be used for inferencing when Production data is read.

Please Note: The currently supported model types are: Sklearn (ML & CV), Keras (ML & CV), and PyTorch (ML).

Check out the walk-through to Register a DSL model to the Data Pipeline (from the Model tab).

Registering a Model from the Model tab

The user can deploy a saved DSL model to the Data Pipeline plugin by using the Model tab.

  • Navigate to the Model tab.

  • Select a model (unregistered model) from the list.

  • Click the Register icon for the model.

Registering a model from the Model tab

  • The Deploy to Pipeline dialog box appears to confirm the action.

  • Click the Yes option.

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  • The selected model gets published and deployed to the Data Pipeline (It disappears from the Unpublished model list).

  • A notification message appears to inform the same.

A confirmation message after the Model gets registered.

  • The model gets listed under the Registered model list.

The Model list displaying the Registered Models

Please Note:

  • The Register option provided under the Notebook tab and Model tab perform the same task.​

  • Refer the Data Science Lab Quick Start Flow page to get an overview of the Data Science Lab module in nutshell. Click here to get redirected to the quick start flow page.

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