Neptune is a platform for collaboration for data scientists and it focuses on tracking metrics such as model training curves, data input, and features calculated. It also helps transform data that's been tracked into a knowledge repository while providing a platform so that we can share, compare, and discuss work that's been done in a data science project.

Notebooks are an essential tool for data scientists, regardless of their area of specialization. They allow data scientists to work interactively, keeping code and results—such as visualizations—in a single document. Neptune builds on top of this experience and comes with Jupyter and JupyterLab extensions that let you track notebooks in Neptune.

The following are the key features of notebooks in Neptune:

• In Neptune, each notebook consists of a collection of checkpoints that you upload directly from the Jupyter user interface.
• In the project, an unlimited number of notebooks and checkpoints are allowed.
• Browse checkpoints history across all the notebooks in the project (https://ui.neptune.ml/shared/onboarding/notebooks).

• Share a notebook as a link (https://ui.neptune.ml/shared/onboarding/n/neural-style-tutorial-c96dce51-409a-4b1b-8dbf-c47d52868d9b/9a7f6736-8794-44f0-9060-cf1b451d92d9).
• Compare two notebooks side-by-side, such as source code (https://ui.neptune.ml/o/shared/org/onboarding/compare-notebooks?sourceNotebookId=e11f2bd6-6bb5-4269-b3d7-84453ad19ddb&sourceCheckpointId=a4ed1ff3-0d5d-4d59-b1d7-60edc4f140b6&targetNotebookId=e11f2bd6-6bb5-4269-b3d7-84453ad19ddb&targetCheckpointId=60911a35-6ee2-40c7-af10-8a7c8a79e6cb).