Machine Learning API challenges ahead

Here is a summary of the ML challenges to reach mass market with APIs:

1. Simplify the preprocessing (data cleaning, features extraction & selection-> 90% work) & integration into a mining or ML (10%) & http://scikit-learn.org (opensource project supported by google &  INRIA research group)
2. Simplification of data visualization
3. Simplification of semi supervised tagging (reduce the tagging/labelling effort) 
4. Simplification of parameter selection (model included): Hyperopt: A Python library for optimizing machine learning algorithms; SciPy 2013 - YouTube
5. For services in the cloud, the biggest show stopper is data transfer (way to slow) & confidentiality. 

Players to watch: 

Alchemy API 

PredictionIO : open source machine learning server 

Google prediction API

Apache Mahout: Scalable Machine Learning and data-mining