> For the complete documentation index, see [llms.txt](https://docs.bdb.ai/data-science-lab-6/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-6/notebook-operations.md).

# Notebook Operations

{% hint style="info" %}
*<mark style="color:green;">Please Note:</mark> The **Notebook Operations** may differ based on the selection of the project environments. A notebook created under the **PySpark** environment only supports **Data**, **Secrets**, **Variable Explorer**, and **Writers** operations.*
{% endhint %}

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-6/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.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.bdb.ai/data-science-lab-6/notebook-operations.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.