Flink-ML constitutes the machine learning library of Apache Flink. Our vision is to make machine learning easily accessible to a wide audience and yet to achieve extraordinary performance. For this purpose, Flink-ML is based on two pillars:

Flink-ML contains implementations of popular ML algorithms which are highly optimized for Apache Flink. Theses implementations allow to scale to data sizes which vastly exceed the memory of a single computer. Flink-ML currently comprises the following algorithms:

• Classification ** Soft-margin SVM
• Regression ** Multiple linear regression
• Recommendation ** Alternating least squares (ALS)

Since most of the work in data analytics is related to post- and pre-processing of data where the performance is not crucial, Flink wants to offer a simple abstraction to do that. Linear algebra, as common ground of many ML algorithms, represents such a high-level abstraction. Therefore, Flink will support the Mahout DSL as a execution engine and provide tools to neatly integrate the optimized algorithms into a linear algebra program.

Flink-ML has just been recently started. As part of Apache Flink, it heavily relies on the active work and contributions of its community and others. Thus, if you want to add a new algorithm to the library, then find out how to contribute and open a pull request!