The Machine Learning Group at the University of Waikato helped shape the open-source community in the machine learning space with its well-known machine learning workbench WEKA. WEKA was mainly aimed at batch-processing, though it also had some support for incremental learning and processing of data. Later on, MOA was developed to provide an optimized framework for data stream learning, which has become very popular in the open-source community.

Since the mid 2000s, the Applied Machine Learning Group has been active in the commercial space, offering not only general machine learning consulting, but also software for processing spectral data (managing data, building models, making predictions, tying into business processes). During that time, in order to prototype machine learning applications faster, the ADAMS framework was born. ADAMS consists of more powerful data science tools than WEKA had to offer and a workflow engine for automating machine learning tasks that can be integrated into business processes.

Artificial Intelligence (AI), or to be more precise Deep Learning, has been adopted as another tool for solving challenging problems successfully across data domains (e.g., spectral data, image processing). Deep Learning techniques can complement more traditional machine learning ones and we choose the most appropriate one based on the constraints of a project.