There’s a flurry of Machine Learning platforms/languages/libraries/systems that all implements almost the same algorithms. Have you tried them ? I’m wondering which one is the best to express a new algorithm quickly, efficiently and scalable.

For what I’ve seen, SciKit and MLBase sounds like the best choices from the usability point of view, GraphLab, MLBase and Mahout are great on scalability, and GraphLab is the most efficient, with SciKit and MLBase just on its trail.

Also it seems that GraphLab is not super easy to deploy.

GraphLab and (obviously) SciKit can interface with iptyhon notebook. What about MLBase ? At least Spark has Scala and Python bindings, so it should be able to connect.

SciKit-learn : http://scikit-learn.org GraphLab : http://graphlab.org/ MLBase : http://www.mlbase.org/ Mahout : http://mahout.apache.org/

Any other solutions ?