Needle in the Haystack: Applied ML for Inspections of Power Infrastructure
Part of the Data & AI specialist track
What do you do when you’ve captured millions of aerial images across thousands of kilometers and want to find the handful of problems that could take out a state’s power supply? This talk explores how we built a full-stack system — combining machine learning, Django, and self hosted infrastructure — to identify critical anomalies in Australia’s electricity transmission network.
You’ll learn how we perform image processing at scale, reducing an overwhelming data problem into a handful of insights that matter and how we present these results to users.
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I want a ticket!Many of Australia’s overhead transmission lines are aging, and targeted maintenance is crucial to prevent failures. At scale, this means analysing millions of high-resolution images to detect rare and subtle issues. This talk walks through a real-world application that uses Machine Learning and Django to turn this data problem into something manageable, finding the needles in the haystack.
We’ll look at: • How aerial inspection imagery is processed using machine learning pipelines • How we host and manage large volumes of images (~400 TB) • The architecture of a Django-based web portal that turns model outputs into customer-facing insights • How we render dashboards, data tables, and generate structured PDF reports • Lessons learned from managing complexity, performance, and usability at scale
This is a practical and technical talk, best suited to software engineers, data practitioners, and ML engineers interested in building end-to-end systems using Python.
Boaz is a software engineer with an interest in applied machine learning. He has previously worked in control systems, working on gimbals and self-balancing bicycles. He loves Python and working with Django.