Machine Learning Times
Machine Learning Times
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10 months ago
How Machine Learning Gave Australia An Edge in the Tokyo Pool

Originally published in The Sydney Morning Herald, Aug 12, 2021.

When Cate Campbell touched the wall ahead of American Abbey Weitzeil in the final leg of the women’s medley relay in Tokyo, it signified the end of a record-breaking meet for the Australian team at the Olympic Games.

Individual swims would account for seven of the nine gold medals as the rising stars and seasoned racers of the squad rose to the occasion. Yet it would be a return of six medals across seven relays, the most of any competing nation, that underpinned the group effort from the first day until the last.

Much of that success came from the quality of the swimmers, the planning from coaches and hours spent working on split-second changeovers, one of which was an 0.04 second piece of daredevilry from Campbell that all but clinched gold in her desperate struggle against Weitzeil.

But part of it arose from a world-leading collaboration between Swimming Australia’s high-performance specialists and tech giant Amazon, which used machine learning to not only suggest the best possible combinations for Australia but predict with uncanny accuracy the likely squads to be deployed by rivals.

Swimming Australia signed a deal with Amazon Web Services midway through 2019 for cloud and data solutions. For Jess Corones, SA’s performance solutions manager, it didn’t mean a great deal; when she thought of Amazon, she thought of online shopping.

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