Notebook Operations

This section aims at describing the various operations for a Data Science Notebook.

Please Note: The Notebook Operations may differ based on the selection of the project environments.

A Data Science Notebook created under the PyTorch or TensorFlow environment will contain the following operations:

• Data: Add data and get a list of all the added datasets.

• ​Secrets: You can generate Environment Variables to save your confidential information from getting exposed.

• ​Algorithms: You can get steps to do Algorithm Settings and Project-level access to use Algorithms inside Notebook.

• ​Transforms: Save and load models with transform script, register them, or publish them as an API through the DS Lab module.

• ​Models: You can train, save, and load the models (Sklearn, Keras/TensorFlow, PyTorch). You can also register a model using this tab. Refer to Model Creation using Data Science Notebook for more details.

• Artifacts: You can save the plots and datasets as Artifacts inside a DS Notebook.

• ​Variable Explorer: Get detailed information on Variables declared inside a Notebook.

• Writers: Write the DSL experiments' output into the database writers' supported range.