b*****o 发帖数: 715 | 1 Machine Learning:
The High-Interest Credit Card of Technical Debt
http://static.googleusercontent.com/media/research.google.com/e
其中我很感慨的就是下面这段:自己重写一个专用的工具比开源的通用工具靠谱。
It may be surprising to the academic community to know that only a tiny
fraction of the code in
many machine learning systems is actually doing “machine learning”. When
we recognize that a
mature system might end up being (at most) 5% machine learning code and (at
least) 95% glue code,
reimplementation rather than reuse of a clumsy API looks like a much better
strategy.
我自己加的推论就是,production的ML系统,千万不能用python来写,虽然刚上手的时
候很爽,但是太难维护和扩展了。 |
d******e 发帖数: 7844 | 2 有用Python来做产品的?
at
【在 b*****o 的大作中提到】 : Machine Learning: : The High-Interest Credit Card of Technical Debt : http://static.googleusercontent.com/media/research.google.com/e : 其中我很感慨的就是下面这段:自己重写一个专用的工具比开源的通用工具靠谱。 : It may be surprising to the academic community to know that only a tiny : fraction of the code in : many machine learning systems is actually doing “machine learning”. When : we recognize that a : mature system might end up being (at most) 5% machine learning code and (at : least) 95% glue code,
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Z**0 发帖数: 1119 | 3 Oh my god. 95% glue code.
要么是里边有很多machine learning module,需要glue together。更加可能是,采集
的数据,需要很多步骤处理,才可以用在ML的module上面,最后还有presentation
result部分。
是不是大部分DS在公司很多工作都是和clean data和present final result有关了?
at
【在 b*****o 的大作中提到】 : Machine Learning: : The High-Interest Credit Card of Technical Debt : http://static.googleusercontent.com/media/research.google.com/e : 其中我很感慨的就是下面这段:自己重写一个专用的工具比开源的通用工具靠谱。 : It may be surprising to the academic community to know that only a tiny : fraction of the code in : many machine learning systems is actually doing “machine learning”. When : we recognize that a : mature system might end up being (at most) 5% machine learning code and (at : least) 95% glue code,
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b*****o 发帖数: 715 | 4 Machine Learning:
The High-Interest Credit Card of Technical Debt
http://static.googleusercontent.com/media/research.google.com/e
其中我很感慨的就是下面这段:自己重写一个专用的工具比开源的通用工具靠谱。
It may be surprising to the academic community to know that only a tiny
fraction of the code in
many machine learning systems is actually doing “machine learning”. When
we recognize that a
mature system might end up being (at most) 5% machine learning code and (at
least) 95% glue code,
reimplementation rather than reuse of a clumsy API looks like a much better
strategy.
我自己加的推论就是,production的ML系统,千万不能用python来写,虽然刚上手的时
候很爽,但是太难维护和扩展了。 |
d******e 发帖数: 7844 | 5 有用Python来做产品的?
at
【在 b*****o 的大作中提到】 : Machine Learning: : The High-Interest Credit Card of Technical Debt : http://static.googleusercontent.com/media/research.google.com/e : 其中我很感慨的就是下面这段:自己重写一个专用的工具比开源的通用工具靠谱。 : It may be surprising to the academic community to know that only a tiny : fraction of the code in : many machine learning systems is actually doing “machine learning”. When : we recognize that a : mature system might end up being (at most) 5% machine learning code and (at : least) 95% glue code,
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Z**0 发帖数: 1119 | 6 Oh my god. 95% glue code.
要么是里边有很多machine learning module,需要glue together。更加可能是,采集
的数据,需要很多步骤处理,才可以用在ML的module上面,最后还有presentation
result部分。
是不是大部分DS在公司很多工作都是和clean data和present final result有关了?
at
【在 b*****o 的大作中提到】 : Machine Learning: : The High-Interest Credit Card of Technical Debt : http://static.googleusercontent.com/media/research.google.com/e : 其中我很感慨的就是下面这段:自己重写一个专用的工具比开源的通用工具靠谱。 : It may be surprising to the academic community to know that only a tiny : fraction of the code in : many machine learning systems is actually doing “machine learning”. When : we recognize that a : mature system might end up being (at most) 5% machine learning code and (at : least) 95% glue code,
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l********e 发帖数: 220 | 7 为啥不能用python?看不出来你这个推论怎么来的?不用python 用啥?C++, R?有什么
区别?R的ML API也很多把?
at
【在 b*****o 的大作中提到】 : Machine Learning: : The High-Interest Credit Card of Technical Debt : http://static.googleusercontent.com/media/research.google.com/e : 其中我很感慨的就是下面这段:自己重写一个专用的工具比开源的通用工具靠谱。 : It may be surprising to the academic community to know that only a tiny : fraction of the code in : many machine learning systems is actually doing “machine learning”. When : we recognize that a : mature system might end up being (at most) 5% machine learning code and (at : least) 95% glue code,
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