Collaborate with your team using data science notebooks
summary
Claim: collaborative notebooks (deepnote) let teams go from siloed code to shared reproducible work. Demo shows deepnote combining SQL python R and no code visualization blocks, an AI Suite that generates and edits notebooks, plus 50 native Integrations. Examples include a working random forest classifier, version history with rollbacks, GitHub push and app/dashboard export with link sharing. Advice: require cloud notebooks, parameterize inputs, track experiments and tests; the platform demoed these features and the app sharing and versioning appear to work.
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chapters (16)
- 0:00Collaboration with notebooks
- 1:51What is Deepnote
- 4:41Notebooks for citizen data scientists
- 7:12History of notebooks
- 9:38Who uses modern data science notebooks
- 11:03Why collaboration fails
- 13:17Best practices for team notebooks
- 16:27Dev practices for production notebooks
- 18:38Embedded data science vision
- 19:02Q&A: Git with notebooks
- 20:27Q&A: Versioning data
- 21:56Q&A: Kernel configuration
- 23:57Q&A: Sharing workspaces
- 25:44Q&A: Git integration
- 26:05Q&A: Notebook apps
- 28:09Closing remarks
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