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by matheist
1850 days ago
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> 2) all the variables that are in scope in the Python session at deployment time. My eyebrows raised in surprise at this point! Obviously there's a tradeoff between convenience ("Oooh it's working! great let me deploy right now") and reproducibility ("Hmm I need to re-deploy what my teammate was working on because <reason>, let me go and find their notebook... ah here it is, oh weird why do I have this undefined variable") and this is hard on the convenience side. Was it surprising to you too that you'd have users who wanted this location on the continuum? |
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> Was it surprising to you too that you'd have users who wanted this location on the continuum?
I’m still working on the “have users” piece, but there were signals for wanting more simplicity/convenience in conversations that I had with data scientists. I also felt as a data scientist that existing deployment solutions sometimes felt over-engineered for flexibility and reproducibility, at the cost of convenience. I'm exploring the other end of the continuum with Convect -- how opinionated can we get with ML workflows to simplify and still be useful?