Machine Learning holds the potential to unlock actionable insights from your data.  Many business decisions are based on a 'hunch' about what influences an outcome, such as which mortgage applications contain too much risk.  With machine learning, however, unknown connections and relationships are highlighted.

Many vendors are offering Machine Learning (ML).  There are open source initiatives and multiple products on the market.  What sets one product apart from the others?  How do you avoid the pitfall of unstable models, or too many false positives?  Robust validation is the solution.

CeeqIT has partnered with a Waterloo company named Polyalgorithm Machine Learning. PolyML has developed a comprehensive ML Framework.  Using Clarvoynt as the data integration platform, data is assembled and prepared for analysis.  This training data set is passed to PolyML for preparation. Proprietary techniques are employed by the PolyML team to ensure your data is fit to purpose. For model building, multiple techniques can be employed, depending on the nature of the data (Neural Networks, Nearest Neighbour, etc.) The PolyML Framework tests and stresses a collection of candidate models to winnow those that are not robust.  Furthermore, the framework moves the data through successive validation to ensure the best results possible.

PolyML boasts a series of successful projects that include a wide range of applications, from financial sector information to streams of data from hyper-spectral cameras. 
Challenge us with one small project and let CeeqIT and PolyML show you the results.