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.
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