National Machine Learning Community of Practice
Ms Komathy Padmanabhan1, Ms Kiowa Scott Hurley1, Dr Nick Hamilton2
1Monash University, Clayton, Australia, 2University of Queensland, Australia
From drug discovery to image analysis and robotics to social sciences, machine learning is transforming science. Alongside the growth of big data, advances in imaging, and new technologies that enable the characterisation and study of materials at a molecular level, machine learning is an essential tool that allows researchers and clinicians to analyse and learn from these huge amounts of information.
Machine learning offers capacity to parse large datasets, detect patterns and associations that may be too subtle for human minds to pick up, and learn from those associations making it an invaluable tool.But applying or developing machine learning approaches come with some major challenges. A recent survey by Monash University found that many researchers keen to apply machine learning are limited by the enormous computing power required, while others find that applying machine-learning isn’t as straightforward or user-friendly as they need it to be.
This talk is about the joint venture between ARDC, Monash University and University of Queensland on the Environments to Accelerate Machine Learning Based Discovery initiative. The initiative aims to make the access to high performance computing services easier, train the researchers to make the most effective use of ML tools and libraries and to build a community of practice around Machine Learning.
As an outcome, National Machine Learning Community of Practice (ML4AU) was launched in October 2020 to bring the collaborative network of researchers, ML practitioners and eResearch groups interested to collaborate over the emerging needs in ML capabilities and expertise.
Komathy leads the Data Science, AI and Sensitive Research Data Platforms at the Monash Technology Research Platforms. Komathy is passionate about building capabilities to enable application of Machine Learning and Artificial Intelligence to research across various domains, with specific interests around sensitive research data.