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Scaling Data Labeling with Machine Learning

Posted Jun 21
# Keynote
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SPEAKER
Felix Lau
Felix Lau
Felix Lau
Research Engineer in ML @ Scale AI

Felix Lau is a Research Engineer in the Machine Learning Team where he works on improving the speed and quality of data annotation through prelabeling, interactive tooling, and quality assurance ML models. Prior to Scale, Felix is a Senior Machine Learning Scientist at Arterys and developed numerous FDA-cleared ML models to speed up the workflow of radiologists. He received his BEng in Computer Science from Hong Kong University of Science and Technology.

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Felix Lau is a Research Engineer in the Machine Learning Team where he works on improving the speed and quality of data annotation through prelabeling, interactive tooling, and quality assurance ML models. Prior to Scale, Felix is a Senior Machine Learning Scientist at Arterys and developed numerous FDA-cleared ML models to speed up the workflow of radiologists. He received his BEng in Computer Science from Hong Kong University of Science and Technology.

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SUMMARY

At Scale AI, we label on the order of 10MM annotations per week. Our data is diverse both in image space (e.g. cameras, weather conditions, driving surface) and label space (e.g. object and attributes categories). In this talk, Felix Lau discusses how we leverage machine learning to scale a customer-specific, problem-specific, real-time data labeling solution.

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