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, 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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What is Practical AI?
Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more).
The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!