Saturday, October 19, 2013

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%) & (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: 
PredictionIO : open source machine learning server 
Apache Mahout: Scalable Machine Learning and data-mining

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