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Reduce bushfire risk with machine learning at scale (Level 300 | Advanced)

With increasingly extreme weather events, bushfires have become a real risk globally. Energy operators need to closely monitor and manage vegetation growth around their energy network infrastructure, spanning tens of thousands of kilometres. Geomatic.AI use machine learning to improve the efficiency of its vegetation management solution, reducing the need for manual identification of vegetation and power lines by up to 80%. In this session, learn how Geomatic.AI uses Amazon SageMaker Processing, Amazon SageMaker Pipelines, and AWS Batch to build, train, and deploy batch processing machine learning models that identify vegetation growth in proximity to power lines.

Speakers:
Derrick Choo, Solutions Architect, AWS
Nathanael Weldon, Geospatial AI Specialist, Geomatic.AI

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Sustainability powered by AI and machine learning (Level 200 | Intermediate)
Sustainability powered by AI and machine learning (Level 200 | Intermediate)

Sustainability is top of mind for leaders today and spans a multitude of applications, ranging from decarbo...

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Sepsis detection using model ensembles on Amazon SageMaker (Level 300 | Advanced)
Sepsis detection using model ensembles on Amazon SageMaker (Level 300 | Advanced)

Sepsis is one of the leading causes of death in hospital patients worldwide. In this session, learn how NSW...