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Debugging Model Performance with Scale Nucleus

Posted Jun 21
# Keynote
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SPEAKER
Russell Kaplan
Russell Kaplan
Russell Kaplan
Head of Nucleus at Scale AI

Russell Kaplan leads Scale Nucleus, Scale's Dataset IDE for machine learning engineers. He was previously founder and CEO of Helia AI, a computer vision startup for real-time video understanding, which Scale acquired in 2020. Before that, Russell was a senior machine learning scientist on Tesla's Autopilot team, and he received his M.S. and B.S. from Stanford University, where he was a researcher in the Stanford Vision Lab advised by Fei-Fei Li.

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Russell Kaplan leads Scale Nucleus, Scale's Dataset IDE for machine learning engineers. He was previously founder and CEO of Helia AI, a computer vision startup for real-time video understanding, which Scale acquired in 2020. Before that, Russell was a senior machine learning scientist on Tesla's Autopilot team, and he received his M.S. and B.S. from Stanford University, where he was a researcher in the Stanford Vision Lab advised by Fei-Fei Li.

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SUMMARY

In this Tech Talk, we show how you can achieve the concept of “Operation Vacation” for the models you create, and make sure that the model is testing the subsets that you actually care about with Scale’s latest product, Scale Nucleus.

Using PandaSet, a multimodal dataset for autonomous driving, we demonstrate how you can easily debug model performance and automatically refine your model. In the process we also dive into Nucleus’s features to show how to curate sub datasets and edge cases easily with custom metrics, image similarity search, and auto-pivot, automatically augmenting the data collection to accelerate machine learning training process.

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# Scale Rapid
# Tech Talk
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