0:00-47:33
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Production ML systems include more than just the model. In these complicated systems, how do you ensure quality over time, especially when you are constantly updating your infrastructure, data and models? Tania Allard joins us to discuss the ins and outs of testing ML systems. Among other things, she presents a simple formula that helps you score your progress towards a robust system and identify problem areas.
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Featuring:
- Tania Allard – Website, GitHub, X
- Chris Benson – Website, GitHub, LinkedIn, Bluesky, X
- Daniel Whitenack – Website, GitHub, X
Show Notes:
- “What’s your ML score” talk
- “Jupyter Notebooks: Friends or Foes?” talk
- Joel Grus’s episode: “AI code that facilitates good science”
- Papermill
- nbdev
- nbval
Books
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changelog.zulipchat.comChangelog++
changelog.comWebsite
bitsandchips.meGitHub
github.comX
x.comWebsite
chrisbenson.comGitHub
github.comLinkedIn
linkedin.comBluesky
bsky.appX
x.comWebsite
datadan.ioGitHub
github.comX
x.com“What’s your ML score” talk
youtu.bePapermill
papermill.readthedocs.ionbdev
fast.ainbval
github.com“DevOps For Dummies” by Emily Freeman
amazon.comPRs welcome!
github.com
