{"action":"created","comment":{"url":"https://api.github.com/repos/apache/flink/pulls/comments/30784160","id":30784160,"diff_hunk":"@@ -20,8 +20,100 @@ specific language governing permissions and limitations\n under the License.\n -->\n \n+The Machine Learning (ML) library for Flink is a new effort to bring scalable ML tools to the Flink\n+community. Our goal is is to design and implement a system that is scalable and can deal with\n+problems of various sizes, whether your data size is measured in megabytes or terabytes and beyond.\n+We call this library FlinkML.\n+\n+An important concern for developers of ML systems is the amount of glue code that developers are\n+forced to write [1] in the process of implementing an end-to-end ML system. Our goal with FlinkML\n+is to help developers keep glue code to a minimum. The Flink ecosystem provides a great setting to\n+tackle this problem, with its scalable ETL capabilities that can be easily combined inside the same\n+program with FlinkML, allowing the development of robust pipelines without the need to use yet\n+another technology for data ingestion and data munging.\n+\n+Another goal for FlinkML is to make the library easy to use. To that end we will be providing\n+detailed documentation along with examples for every part of the system. Our aim is that developers\n+will be able to get started with writing their ML pipelines quickly, using familiar programming\n+concepts and terminology.\n+\n+Contrary to other data-processing systems, Flink exploits in-memory data streaming, and natively\n+executes iterative processing algorithms which are common in ML. We plan to exploit the streaming\n+nature of Flink, and provide functionality designed specifically for data streams.\n+\n+FlinkML will allow data scientists to test their models locally and using subsets of data, and then\n+use the same code to run their algorithms at a much larger scale in a cluster setting.\n+\n+We are inspired by other open source efforts to provide ML systems, in particular\n+[scikit-learn](http://scikit-learn.org/) for cleanly specifying ML pipelines, and Spark’s\n+[MLLib](https://spark.apache.org/mllib/) for providing ML algorithms that scale with problem and\n+cluster sizes.\n+\n+We already have some of the building blocks for FlinkML in place, and will continue to extend the\n+library with more algorithms. An example of how simple it is to create a learning model in\n+FlinkML is given below:\n+\n+{% highlight scala %}\n+// LabelbedVector is a feature vector with a label (class or real value)\n+val data: DataSet[LabelVector] = 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[docs]  Add vision and roadmap for ML library to docs","user":{"login":"thvasilo","id":9048995,"avatar_url":"https://avatars.githubusercontent.com/u/9048995?v=3","gravatar_id":"","url":"https://api.github.com/users/thvasilo","html_url":"https://github.com/thvasilo","followers_url":"https://api.github.com/users/thvasilo/followers","following_url":"https://api.github.com/users/thvasilo/following{/other_user}","gists_url":"https://api.github.com/users/thvasilo/gists{/gist_id}","starred_url":"https://api.github.com/users/thvasilo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/thvasilo/subscriptions","organizations_url":"https://api.github.com/users/thvasilo/orgs","repos_url":"https://api.github.com/users/thvasilo/repos","events_url":"https://api.github.com/users/thvasilo/events{/privacy}","received_events_url":"https://api.github.com/users/thvasilo/received_events","type":"User","site_admin":false},"body":"We should have a document describing the vision of the Machine Learning library in Flink and an up to date roadmap.\r\nThis PR provides that, and the text can also be used in the 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