Testing ML systems (Practical AI #74)
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English - January 27, 2020 21:00 - 47 minutes - 65.4 MB - ★★★★ - 28 ratingsTechnology Education How To changelog open source oss software development developer hacker Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
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.
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 – Twitter, GitHub, WebsiteChris Benson – Twitter, GitHub, LinkedIn, WebsiteDaniel Whitenack – Twitter, GitHub, Website
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
“DevOps For Dummies” by Emily Freeman
Something missing or broken? PRs welcome!