Models

This page offers step-by-step process on how to develop, train, save, and load a model inside the Notebook infrastructure.

The Data Science Lab offers an integrated Notebook environment that enables users to build, train, and operationalize machine learning models. This section describes the end-to-end workflow for creating, saving, and loading a Data Science model using Notebook infrastructure.

Please refer to the "Saving a Model" page for the step-by-step process on saving a model using the notebook infrastructure.

Note: All machine learning models successfully saved within the Data Science Lab module—irrespective of their origin (i.e., trained and saved via Notebooks, imported from an external source, or generated through Automated Machine Learning (AutoML))—are systematically cataloged. These models are accessible via the Models list page, which is located within the Left-side navigation menu of the Data Science Lab interface.