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 – 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

• “DevOps For Dummies” by Emily Freeman

Something missing or broken? PRs welcome!