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Accelerated data science with a Kaggle grandmaster
Practical AI: Machine Learning, Data Science, LLM
The Modular Pipeline for Data Science
I would say that rapids, for example, is even more applicable to the real world because there you might have very larger data frames. So I use Docker with a specific pytorch image to have like always the same environment and also can replicate my experiments on different machines. That's all things I learned during the years. And especially in Kaggle, those discussions get a lot of traction as people try to adapt their systems or whatever they are trying to do. It really depends from project to project, I would say, where it's applicable or not.
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