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Comment on Software design patterns for Machine Learning R&D

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This are very sound advices. I have arrived at very similar architecture in LSI environment.

From my experience, here are some advantages of this architecture:

- stages could be independently rewritten, so you could prototype in fast-writing language (perl in my case) and later rewrite whole stages or parts of them in fast-execution language if you need extra performance (C,C++ here);

- you could easily integrate third party software in your workflow - most of the existing tools in the field work with input and output files;

- you could reuse already written stages for different purposes - just pass them different options for input/output and parameters.

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