What are the use cases of a Jupyter Notebook in data science?

  • many use cases (my favourite is the case documented by Netflix).[1]
  • many folks arguing notebooks are an insecure, unmaintainable tooling that should be stripped of use cases.[2]
  • many reasons why you may still ultimately deploy a notebook as part of a productionised process, secure enough & maintainable enough to have meaningful positive impact on your team’s workflow.[3]
  • run them in the cloud eg on SageMaker or Azure Notebooks or AIPN
  • This enables serverless, clustering, horizontal & vertical scaling.
  • It is also vastly more configurable WRT security, than running on your own laptop or hardware locally (eg specify VPCs and SGs).
  • manage their dependencies effectively
  • This means upgrading wherever possible e.g. to guard against vulnerabilities that may creep into the open source components such as Python, Sci-Kit Learn, or nbextensions.
  • It also means having a clear plan as to which packages/libraries won’t be upgraded because you want the specific functionality or syntax of some legacy version eg when patching is preferred.




Bass/BV @StornowayBand. DataCat @AstraZeneca. Consulting via StoneStreetProductions.

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Oli Steadman

Oli Steadman

Bass/BV @StornowayBand. DataCat @AstraZeneca. Consulting via StoneStreetProductions.

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