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Merge pull request #1008 from CodingCat/master

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typo fix
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Nan Zhu 2016-03-18 07:12:14 -04:00
commit a2146708bd
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doc/jvm/xgboost4j-intro.md
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@ -24,7 +24,7 @@ Many of these machine learning libraries(e.g. [XGBoost](https://github.com/dmlc/
requires new computation abstraction and native support (e.g. C++ for GPU computing).
They are also often [much more efficient](http://arxiv.org/abs/1603.02754).
The gap between the implementation fundamentals of the general data processing frameworks and the more specific machine learning libraries/systems prohibits the smooth connection between these two types of systems, thus brings unnecessary inconvenience to the end user. The common workflow to the user is to utilize the systems like Spark/Flink to preprocess/clean data, pass the results to machine learning systems like [XGBoost](https://github.com/dmlc/xgboost)/[MxNet](https://github.com/dmlc/mxnet)) via the file systems and then conduct the following machine learning phase. This process jumping across two types of systems creates certain inconvenience for the users and brings additional overhead to the operators of the infrastructor.
The gap between the implementation fundamentals of the general data processing frameworks and the more specific machine learning libraries/systems prohibits the smooth connection between these two types of systems, thus brings unnecessary inconvenience to the end user. The common workflow to the user is to utilize the systems like Spark/Flink to preprocess/clean data, pass the results to machine learning systems like [XGBoost](https://github.com/dmlc/xgboost)/[MxNet](https://github.com/dmlc/mxnet)) via the file systems and then conduct the following machine learning phase. This process jumping across two types of systems creates certain inconvenience for the users and brings additional overhead to the operators of the infrastructure.
We want best of both worlds, so we can use the data processing frameworks like Spark and Flink toghether with
the best distributed machine learning solutions.