End-to-end implementation of a machine learning pipeline (2017)

Re:End-to-end implementation of a machine learning pipeline ... by DrNuke on Thursday October 25, 2018 @06:54AM (#18299933)

That’s it, really. Any good reference to keep up to date with the last-mile best practices for the average ML practitioner? Thanks!

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Re:End-to-end implementation of a machine learning pipeline ... by rasmi on Thursday October 25, 2018 @01:05PM (#18302439)

I link these resources often, but they are often relevant! See "The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction" [1] and the Rules of Machine Learning [2]. Another classic: "Machine Learning: The High Interest Credit Card of Technical Debt" [3], and recently added: Responsible AI Practices [4].

[1] https://ai.google/research/pubs/pub46555

[2] https://developers.google.com/machine-learning/rules-of-ml/

[3] https://ai.google/research/pubs/pub43146

[4] https://ai.google/education/responsible-ai-practices

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Re:End-to-end implementation of a machine learning pipeline ... by fsavard on Thursday October 25, 2018 @01:19PM (#18302544)

It's a theme I like to read about (I mean "practical issues around ML in production settings"). I find company blogs and some research publications are great resources. Examples:

- https://eng.uber.com/ - https://code.fb.com/

and many more. Google also publishes papers on various engineering practices obviously, some ML-related, but I can't find a blog where they focus on that specifically.

Also it's not "to keep up to date", but there's a great paper (from Google) that's often cited:

Machine Learning: The High Interest Credit Card of Technical Debt https://ai.google/research/pubs/pub43146

It talks about issues you face over the long run (I've experienced some of those). It also provides interesting pointers for further reading, e.g. about "pipeline jungles".

If others have pointers, I'm curious to hear about them as well.

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