The Learning Curve: Model-in-the-Loop Data Curation
Posted Jun 16, 2021 | Views 1.5K
# Tech Talk
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SPEAKER
Sasha Harrison
Software Engineer @ Scale AI
Sasha Harrison is a machine learning engineer on the Content Understanding product team, where she works on building deep neural networks to streamline operations powering data annotation pipelines. Previously, she worked as a software engineer on the Nucleus team building a comprehensive dataset management platform to automate the data work. She holds a Master's Degree 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 machine learning engineer on the Content Understanding product team, where she works on building deep neural networks to streamline operations powering data annotation pipelines. Previously, she worked as a software engineer on the Nucleus team building a comprehensive dataset management platform to automate the data work. She holds a Master's Degree 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.
