Go From Notebook To Pipeline For Your Data Science Projects With Orchest
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.
Announcements
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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?
Keep In Touch
- Rick
- ricklamers on GitHub
- @RickLamers on Twitter
- Yannick
- yannickperrenet on GitHub
Picks
- Tobias
- Rick
- Yannick
Links
- Orchest
- Geoffrey Hinton
- Yann LeCun
- CoffeeScript
- Vim
- GAN == Generative Adversarial Network
- Git
- SQL
- BigQuery
- Software Carpentry
- Google Colab
- Airflow
- Kedro
- nbdev
- Papermill
- MLFlow
- Metaflow
- DVC
- Andrew Ng
- Kubeflow
- Lua
- Caddy
- Traefik
- DAG == Directed Acyclic Graph
- Jupyter Enterprise Gateway
- Streamlit
- Kubernetes
- Dagster
- DBT
- GitLab
- Spark
- ETL
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Activity