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How Scale Uses ML to Improve Label Quality and Labeler Productivity

Posted Oct 06
# TransformX 2021
# Breakout Session
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SPEAKER
Aerin Kim
Aerin Kim
Aerin Kim
Engineering Manager @ Scale AI

Aerin Kim is the Engineering Manager at Scale AI, where she leads the conversion of raw data into high-quality training data, through machine learning. She is fascinated by the science aspect of training data, the one and only input of deep learning that determines the performance of the model. Prior to Scale, Aerin was a Senior Research Software Engineer at Microsoft where she worked on question answering, semantic parsing and training data generation for AI applications. Prior to Microsoft, Aerin received her Masters degree in Operations Research from the Fu Foundation School of Engineering and Applied Science at Columbia University.

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Aerin Kim is the Engineering Manager at Scale AI, where she leads the conversion of raw data into high-quality training data, through machine learning. She is fascinated by the science aspect of training data, the one and only input of deep learning that determines the performance of the model. Prior to Scale, Aerin was a Senior Research Software Engineer at Microsoft where she worked on question answering, semantic parsing and training data generation for AI applications. Prior to Microsoft, Aerin received her Masters degree in Operations Research from the Fu Foundation School of Engineering and Applied Science at Columbia University.

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SUMMARY

Engineering leader, Aerin Kim, will present a brief summary of ongoing ML research at Scale AI, then deep dive into ML linters and other mechanisms that reliably enhance labeler productivity when labeling complex images and scenes. Aerin will showcase complex scenarios like 3D LiDAR bounding box classification as well as 2D semantic segmentation. When assisted by an ML model, labelers typically generate higher quality data for our customers than they might consistently do on their own.

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Posted Oct 06 | Views 571
# TransformX 2021
# Breakout Session
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