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Scale Rapid Demo

Posted Oct 08
# Scale Rapid
# Tech Talk
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SPEAKERS
Zhichun Li
Zhichun Li
Zhichun Li
Head of Scale Rapid, Scale AI

Zhichun Li is the Head of Scale Rapid at Scale AI. She built the team from scratch with a focus on providing the fastest way to production-level quality labels within a day, with no data minimums. As an early employee of the company, she built up the infrastructure for our supply ops system and scaled up our 3D Sensor Fusion product. Prior to Scale AI, Zhichun studied CS at CMU and earned her MBA from Yale SOM.

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Zhichun Li is the Head of Scale Rapid at Scale AI. She built the team from scratch with a focus on providing the fastest way to production-level quality labels within a day, with no data minimums. As an early employee of the company, she built up the infrastructure for our supply ops system and scaled up our 3D Sensor Fusion product. Prior to Scale AI, Zhichun studied CS at CMU and earned her MBA from Yale SOM.

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Tony Wu
Tony Wu
Tony Wu
Product Engineer, Scale AI

Tony Wu is a Product Engineer at Scale AI and Tech Lead of Scale Rapid. He was inspired to join the Scale Rapid team due to the product's huge potential to impact AI development for companies of all sizes. Prior to joining Scale AI, Tony had a diverse set of experiences at companies such as Amazon, Twitter, and Flexport.

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Tony Wu is a Product Engineer at Scale AI and Tech Lead of Scale Rapid. He was inspired to join the Scale Rapid team due to the product's huge potential to impact AI development for companies of all sizes. Prior to joining Scale AI, Tony had a diverse set of experiences at companies such as Amazon, Twitter, and Flexport.

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SUMMARY

Scale Rapid is our newest product launch and recently went into general access. With Scale Rapid, customers can iterate on labeling specs in a matter of hours to produce production-level quality data, with no data minimums. Acquiring high-quality labeled data is often a barrier to iterating quickly on different model setups so we want to offer a solution to help companies of all sizes bootstrap their ML efforts. In this talk, we discuss the technical and operational challenges inherent in achieving high-quality labels for novel use cases and walk through an example of an experimental project Rapid has supported.

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