Bringing together Machine Learning (ML) tools, libraries, and access to data, across large Graphic Processing Unit (GPU) deployments nationally to provide a consolidated platform for ML-based research, deployed at Monash University and University of Queensland. 

The confluence of big data, Machine Learning (ML) techniques and parallel computing is making AI useful across a range of research areas. There is increasing sophistication, insight and accuracy which is driving a strong and growing appetite across research groups for access to ML capacity, services, libraries, expertise and training.

The environment developed for Machine Learning will support core ML tools for preprocessing, annotating, training, and validation, and integrate with software development environments. A national outreach and training program will engage the researcher community and increase knowledge. Join the Machine Learning Community of Practice for Australia.

Start date 10 April 2020
Expected completion date 3 February 2022
Investment by ARDC $484,800
Lead node
1 Integrated Development Environment for ML
Fundamental and applied ML researchers will have integrated access to low and high end GPUs, ML tools & libraries, data duration tools and reference datasets.
2 Efficient Tools, and improve knowledge and support for common tools
We will improve our knowledge about deploying and configuration of the most widely used ML tools and libraries, including Tensorflow, PyTorch, Keras; and Data preprocessing tools, such as Pandas, Matlab and Moa. This will enable quick and efficient customisation, deployment and support of ML tools and libraries and will best leverage the limited hardware available.
3 Training and Outreach
We will facilitate knowledge sharing, best practice frameworks and collaborations through training programs and Communities of Practices across the participating universities.

Core features

Accelerate research
We will provide a friction-free environment to test ML ideas, demonstrate success and sometimes ‘fail fast’
Promote interdisciplinary research
Promotion of interaction across disciplines through collaborations with tools and libraries, and annotated datasets
Improve efficiency
Usage of ML tools will be more efficient, and users will be able to better leverage available hardware, software and data capabilities.

Who is this project for?

  • Researchers who require access to machine learning tools and computing, and are interested in  upskilling their machine learning expertise across a wide range of domains including life sciences, engineering, computer science, and increasingly the humanities and social sciences.
  • Infrastructure providers

What does this project enable?

The project will enable ML researchers and users from a variety of research areas to do their research efficiently and effectively with available reference data and tools, which will greatly facilitate new discoveries. It will also enable infrastructure providers to provide better data and computing infrastructure for supporting ML researchers.

Handy resources

Join the Machine Learning Community of Practice for Australia.

Read an article about launching the Machine Learning Community of Practice for Australia.

View the report of the 2019 ARDC project ‘Machine learning infrastructure deployed at scale: understanding requirements, demand, impact and international best practice.’

Visit the high-performance data processing facility, MASSIVE

Visit the high performance computer at the University of Queensland, Wiener

Visit the Data science and AI platform at Monash University, DSAI

Monash UniversityVisit
Pawsey Supercomputing Centre Visit
University of QueenslandVisit