Software

A key focus of the Artificial Intelligence Institute will be continuing to develop and maintain open-source software.

Our software supports research, innovation and application of artificial intelligence, through fast, reliable and secure development tools.

We are the leading developers of some of the most popular open source tools for machine learning and data mining including WEKA, MOA, and ADAMS, with more than 10 million downloads.

Open source software reduces training costs and provides access to publicly available frameworks, workflow, data sets and models, alongside a community providing monitoring and security.

Waikato Environment for Knowledge Analysis (WEKA)

WEKA (Waikato Environment for Knowledge Analysis) is an open-source machine learning software in JAVA. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.

WEKA has been downloaded more than 10,542,000 times, is the most popular open-source software for Machine Learning in Java, and the most popular tool to learn Machine Learning, thanks to the best-selling book “Data Mining” and MOOC courses.

The software has also been cited in more than 18,000 research and applied data science publications.

WEKA is one of the oldest available machine learning systems, having started development in 1993, and it is still very active in the machine learning / data mining / AI space.

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Accelerated WEKA 

Accelerated WEKA unifies WEKA, with new technologies that leverage the GPU to shorten the execution time of ML algorithms. It has two benefits aimed at users without expertise in system configuration and coding: an easy installation and a GUI that guides the configuration and execution of the ML tasks. Accelerated WEKA is a collection of packages available for WEKA (e.g., WDL4JwekaPython, and wekaRAPIDS). Accelerated WEKA can be easily installed and anyone can extend it to support new tools and algorithms.

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Massive Online Analysis

MOA is the most popular open-source framework for data stream mining, with a very active growing community. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation, that are suitable for data streams, i.e. cases where one doesn’t have the opportunity to re-process the data multiple times.

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CapyMOA

Released in 2024, CapyMOA offers a fast Python interface for state-of-the-art algorithms in data stream machine learning. CapyMOA combines the speed of MOA with the flexibility of Python and the extensive capabilities of Python’s data science ecosystem, including integration with PyTorch and Scikit-learn.

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ADAMS is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes, released under GPLv3.

River is Python package for streaming and machine learning and handles regression, classification and unsupervised learning. Ideal for adhoc tasks like online metrics computation and concept drift detection.

teex is an xAI Python toolbox for evaluating machine learning explanations. It aims to provide a simple toolbox for assessing individual black-box explanations against ground truth.