Best Practices for ML Engineering (2017)
developers.google.com
Best Practices for ML Engineering (2017)
1–10 of 12 posts
Re: Best Practices for ML Engineering (2017)
#2My favorite part is what so many people seem to forget:
Rule #1: Don’t be afraid to launch a product without machine learning.
Just because you can use machine learning doesn’t mean you should.
Re: Best Practices for ML Engineering (2017)
#3My favorite part is what so many people seem to forget: Rule #1: Don’t be afraid to launch a product without machine learning. Just because you can use machine learning doesn’t mean you should.
Also, should be said. Just because you can use the framework and it produces what you expect the first time: Doesn't mean that you're using the technique correctly.
(AI has some very advanced techniques in it and require attention to detail and background knowledge to use them correctly)
Re: Best Practices for ML Engineering (2017)
#4Might be worth noting that this is old [1]. I think either in 2016 or 2017, the same document was posted in PDF form.
Re: Best Practices for ML Engineering (2017)
#5"Track as much as possible in your current system."
Re: Best Practices for ML Engineering (2017)
#6My favorite part is what so many people seem to forget: Rule #1: Don’t be afraid to launch a product without machine learning. Just because you can use machine learning doesn’t mean you should.
Also, should be said. Just because you can use the framework and it produces what you expect the first time: Doesn't mean that you're using the technique correctly. (AI has some very advanced techniques in it and require attention to detail and background knowledge to use them correctly)
Sounds like a disclaimer for any programming. Just because the happy path works doesn't mean you have a finished system.
Re: Best Practices for ML Engineering (2017)
#7woah i had no idea zinkevich worked at google. that guy is really legit.
Re: Best Practices for ML Engineering (2017)
#8The part about a solid pipeline is very important. Recently I talked to someone who had a bad experience detecting phrases using gensim. I always have had a good experience so I was curious. Turns out they were doing OCR on PDFs and feeding the result directly into gensim. As anyone with OCR experience knows, there is often a lot of noise which is definitely going to screw up gensim or any other NLP tool. The quality of your preprocessing is important!
Re: Best Practices for ML Engineering (2017)
#9Might be worth noting that this is old [1]. I think either in 2016 or 2017, the same document was posted in PDF form. [1] http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
Discussed in 2017: https://news.ycombinator.com/item?id=13414776.
Re: Best Practices for ML Engineering (2017)
#10"Track as much as possible in your current system."
But... "don't be evil" now!