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Testing ML systems (Practical AI #74)
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, Website
- Chris Benson – Twitter, GitHub, LinkedIn, Website
- Daniel 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
- “DevOps For Dummies” by Emily Freeman
Something missing or broken? PRs welcome!