Being able to explain your own code a few months after you wrote it is hard. Imagine having to explain the decisions of some AI algorithm a few years after it run! However, it is relatively easy to set up your development workflow to make that possible, as long as you realize that the way we build ML and AI is fundamentally different from traditional software engineering. In a nutshell, it is all about: reproducible research, development and deployment. It is made possible by a clever use of...

Read the full article at Microsoft MSDN Channel 9

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