> For the complete documentation index, see [llms.txt](https://docs.bdb.ai/data-science-lab-5/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bdb.ai/data-science-lab-5/notebook-operations.md).

# Notebook Operations

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

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*<mark style="color:green;">Please Note:</mark> The **Notebook Operations** may differ based on the selection of the project environments.*&#x20;
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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***](/data-science-lab-5/model-creation-using-data-science-notebook.md) 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.