doddle-model is an in-memory machine learning library that can be summed up with three main characteristics:
- it is built on top of Breeze
- it provides immutable estimators that are a doddle to use in parallel code
- it exposes its functionality through a scikit-learn-like API  in idiomatic Scala using typeclasses
How does it compare to existing solutions?
doddle-model takes the position of scikit-learn in Scala and as a consequence, it's much more lightweight than e.g. Spark ML. Fitted models can be deployed anywhere, from simple applications to concurrent, distributed systems built with Akka, Apache Beam or a framework of your choice. Training of estimators happens in-memory, which is advantageous unless you are dealing with enormous datasets that absolutely cannot fit into RAM.
The project is published for Scala versions 2.11, 2.12 and 2.13. Add the dependency to your SBT project definition:
libraryDependencies ++= Seq( "io.github.picnicml" %% "doddle-model" % "<latest_version>", // add optionally to utilize native libraries for a significant performance boost "org.scalanlp" %% "breeze-natives" % "1.0" )
Note that the latest version is displayed in the Latest Release badge above and that the v prefix should be removed from the SBT definition.
For a complete list of code examples see doddle-model-examples.
Performance of implementations is described here. Also, take a peek at what's written in that document if you encounter
java.lang.OutOfMemoryError: Java heap space.