FlinkML is the Machine Learning (ML) library for Flink. It is a new effort in the Flink community, with a growing list of algorithms and contributors. With FlinkML we aim to provide scalable ML algorithms, an intuitive API, and tools that help minimize glue code in end-to-end ML systems. You can see more details about our goals and where the library is headed in our vision and roadmap here.
FlinkML currently supports the following algorithms:
You can check out our quickstart guide for a comprehensive getting started example.
If you want to jump right in, you have to set up a Flink program.
Next, you have to add the FlinkML dependency to the pom.xml
of your project.
Note that FlinkML is currently not part of the binary distribution. See linking with it for cluster execution here.
Now you can start solving your analysis task. The following code snippet shows how easy it is to train a multiple linear regression model.
A key concept of FlinkML is its scikit-learn inspired pipelining mechanism. It allows you to quickly build complex data analysis pipelines how they appear in every data scientist’s daily work. An in-depth description of FlinkML’s pipelines and their internal workings can be found here.
The following example code shows how easy it is to set up an analysis pipeline with FlinkML.
One can chain a Transformer
to another Transformer
or a set of chained Transformers
by calling the method chainTransformer
.
If one wants to chain a Predictor
to a Transformer
or a set of chained Transformers
, one has to call the method chainPredictor
.
The Flink community welcomes all contributors who want to get involved in the development of Flink and its libraries. In order to get quickly started with contributing to FlinkML, please read our official contribution guide.