About the talk
The Statoil Iceberg Classifier Challenge in 2018 was Kaggle’s most popular image classification challenge in terms of competing teams and was ranked one of the top challenges among all data types at the time. In this session, Kaggle Grandmaster and the 1st place solution developer, David Austin, revisits the challenge with modern day state-of-the-art technologies to evaluate how adjustment of techniques and tools during experimentation could deliver higher performance — even beyond what 1st place achieved.
In this talk, David explores the modeling problem in greater depth and reveals how SigOpt hyperparameter optimization, results monitoring, and artifact tracking contribute to deriving novel insights on the model and uncovering higher-performing configurations for this particular classification challenge. Gain insights on classification tasks, Kaggle competitions, and experimentation processes that can carry into any modeling task or domain, especially regarding how best practices for experimentation impact performance.
David Austin is a Senior Principal Engineer at Intel Corporation working on AI based solutions for the industrial Internet-of-Things and edge segment. He is currently focusing on developing AI workflows for industrial anomaly detection and federated learning. In the past, he has worked on advanced semiconductor manufacturing process integration among other use cases. David is a Kaggle Grandmaster and has been ranked as high as #11 in the world Kaggle competition rankings.View the profile
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