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Congrats on the launch. I'm a MLOps consultant that helps enterprises with productionizing their models on cloud platforms. Previously, also a startup founder w
by ensemblehq 5y ago
Congrats on the launch. I'm a MLOps consultant that helps enterprises with productionizing their models on cloud platforms. Previously, also a startup founder who iterated in the same space and can probably exchange notes.
The problem is definitely a time-consuming and costly one and I'm intrigued to play around with Ploomber. How does Ploomber handle collaboration/code sharing across data scientists?
- idomi 5y agoWe took an approach of keeping the notebook interface/IDE, and behind the scenes Ploomber converts it into .py files so you can collaborate with teammates through Git. The users can still open those as a notebook and interact with the files regularly.
- edublancas 5y agoWe allow people to write pipeline tasks in .py files but open them as notebooks in Jupyter. So they keep the same workflow they're used to but under the hood, they're writing .py files - so they can do code reviews (jupytext handles the .py to .ipynb conversion). Also, when executing the pipeline, ploomber generates an output report for each script, so teams can use this to review any outputs generated by the code. Finally, since the pipeline is modularized, it's easier to split the work. Some people may work in data cleaning, others in feature engineering, and they can all orchestrate the pipeline with "ploomber build". You can read more about our approach in this guest blog post we published a few months ago on the Jupyter blog: https://blog.jupyter.org/ploomber-maintainable-and-collaborative-pipelines-in-jupyter-acb3ad2101a7 https://blog.jupyter.org/ploomber-maintainable-and-collabora... We'd love to hear from your experience! Please send me an email to eduardo@ploomber.io