Go From Notebook To Pipeline For Your Data Science Projects With Orchest

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March 1st, 2021

44 mins 24 secs

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About this Episode

Summary

Jupyter notebooks are a dominant tool for data scientists, but they lack a number of conveniences for building reusable and maintainable systems. For machine learning projects in particular there is a need for being able to pivot from exploring a particular dataset or problem to integrating that solution into a larger workflow. Rick Lamers and Yannick Perrenet were tired of struggling with one-off solutions when they created the Orchest platform. In this episode they explain how Orchest allows you to turn your notebooks into executable components that are integrated into a graph of execution for running end-to-end machine learning workflows.

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  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
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  • Your host as usual is Tobias Macey and today I’m interviewing Rick Lamers and Yannick Perrenet about Orchest, a development environment designed for building data science pipelines from notebooks and scripts.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by giving an overview of what Orchest is and the story behind it?
  • Who are the users that you are building Orchest for and what are their biggest challenges?
    • What are some examples of the types of tools or workflows that they are using now?
  • What are some of the other tools or strategies in the data science ecosystem that Orchest might replace? (e.g. MLFlow, Metaflow, etc.)
  • What problems does Orchest solve?
  • Can you describe how Orchest is implemented?
    • How have the design and goals of the project changed since you first started working on it?
  • What is the workflow for someone who is using Orchest?
  • What are some of the sharp edges that they might run into?
  • What is the deployable unit once a pipeline has been created?
    • How do you handle verification and promotion of pipelines across staging and production environments?
  • What are the interfaces available for integrating with or extending Orchest?
    • How might an organization incorporate a pipeline defined in Orchest with the rest of their data orchestration workflows?
  • How are you approaching governance and sustainability of the Orchest project?
  • What are the most interesting, innovative, or unexpected ways that you have seen Orchest used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while building Orchest?
  • When is Orchest the wrong choice?
  • What do you have planned for the future of the project and company?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA