At the recent GOTO conference in Berlin, Mahout committer Sebastian Schelter outlined recent advances in Mahout's ongoing effort to create a scalable foundation for data analysis that is as easy to use as R or Python.
The need for machine-learning techniques like clustering, collaborative filtering, and categorization has steadily increased the last decade along with the number of solutions needing quick and efficient algorithms to transform vast amounts of raw data into relevant information. Apache Mount 0.3 has been announced on March, adding more functionality, stability and performance.
The Apache Mahout project, a set of highly scalable machine-learning libraries, recently announced it's first public release. InfoQ spoke with Grant Ingersoll, co-founder of Mahout and a member of the technical staff at Lucid Imagination, to learn more about this project and machine learning in general.