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The Learning Curve: Model-in-the-Loop Data Curation

Posted Jun 16
# Tech Talk
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SPEAKER
Sasha Harrison
Sasha Harrison
Sasha Harrison
Software Engineer @ Scale AI

Sasha Harrison is a software engineer on the Nucleus team, where she works on building a comprehensive dataset management platform to automate the data work. Previously, she was a Master’s student in Computer Science at Stanford University with a specialization in AI and research interests in algorithmic fairness (particularly in computer vision and speech recognition).

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Sasha Harrison is a software engineer on the Nucleus team, where she works on building a comprehensive dataset management platform to automate the data work. Previously, she was a Master’s student in Computer Science at Stanford University with a specialization in AI and research interests in algorithmic fairness (particularly in computer vision and speech recognition).

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SUMMARY

Scale Nucleus was developed to help ML teams debug their models by debugging their datasets. In this talk, we walk through some of the latest updates and discuss how Nucleus enables you to diagnose model failure cases, to then prioritize what data should be labeled next to improve model performance. We will then walk through a demo of how you can use models to debug data, and data to debug models for model-in-the-loop data curation.

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Posted Jul 14 | Views 645
# Tech Talk
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