ECL-ML Machine Learning Module

An extensible set of fully parallel Machine Learning (ML) and Matrix processing algorithms to assist with business intelligence; covering supervised and unsupervised learning, document and text analysis, statistics and probabilities, and general inductive inference related problems.


 

The ML project is designed to create an extensible library of fully parallel machine learning routines. This library leverages the distributed nature of the HPCC Systems architecture, providing for extreme scalability to both, the high level implementation of the machine learning algorithms and the underlying matrix algebra library, extensible to tens of thousands of features on billions of training examples.

The existing code is in beta and testing for different use cases continues.