Declarative Machine Learning Language

References

http://apache.github.io/incubator-systemml/dml-language-reference.html#introduction
- QUOTE: SystemML compiles scripts written in Declarative Machine Learning (or DML for short) into MapReduce jobs. DML’s syntax closely follows R, thereby minimizing the learning curve to use SystemML. Before getting into detail, let’s start with a simple Hello World program in DML. Assuming that Hadoop is installed on your machine or cluster, place SystemML.jar and SystemML-config.xml into your directory. Now, create a text file “hello.dml” containing following code:
http://apache.github.io/incubator-systemml/dml-and-pydml-programming-guide.html#overview
- QUOTE: SystemML enables flexible, scalable machine learning. This flexibility is achieved through the specification of a high-level declarative machine learning language that comes in two flavors, one with an R-like syntax (DML) and one with a Python-like syntax (PyDML).
  Algorithm scripts written in DML and PyDML can be run on Hadoop, on Spark, or in Standalone mode. No script modifications are required to change between modes. SystemML automatically performs advanced optimizations based on data and cluster characteristics, so much of the need to manually tweak algorithms is largely reduced or eliminated.
  This SystemML Programming Guide serves as a starting point for writing DML and PyDML scripts.