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Model inspection and interpretation at Seldon (Practical AI #48)
Jun 17, 2019 · 43 min · 63.1 MB
0:00-43:44
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Interpreting complicated models is a hot topic. How can we trust and manage AI models that we can’t explain? In this episode, Janis Klaise, a data scientist with Seldon, joins us to talk about model interpretation and Seldon’s new open source project called Alibi. Janis also gives some of his thoughts on production ML/AI and how Seldon addresses related problems.
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Featuring:
- Janis Klaise – GitHub, LinkedIn, X
- Chris Benson – Website, GitHub, LinkedIn, Bluesky, X
- Daniel Whitenack – Website, GitHub, X
Show Notes:
Books
Something missing or broken? PRs welcome!
Alibi
github.comJoin the discussion
changelog.zulipchat.comChangelog++
changelog.comDigitalOcean
do.coDataEngPodcast
dataengineeringpodcast.comFastly
fastly.comGitHub
github.comLinkedIn
linkedin.comX
x.comWebsite
chrisbenson.comGitHub
github.comLinkedIn
linkedin.comBluesky
bsky.appX
x.comWebsite
datadan.ioGitHub
github.comX
x.comSeldon
seldon.ioSeldon Core
github.com“The Foundation Series” by Isaac Asimov
amazon.com“Interpretable Machine Learning” by Christoph Molnar
christophm.github.ioPRs welcome!
github.com