33: ML is a Data Quality Problem with Peter Gao from Aquarium Learning

On this week's episode of The Data Stack Show, Eric and Kostas talk with Peter Gao, co-founder, and CEO at Aquarium Learning. A former engineer at Cruise Automation, Peter and Aquarium Learning help ML teams improve their model performances by improving their data.

Highlights from this week's episode include:

• How getting hit by a drunk driver made researching self-driving cars personal for Peter (2:12)
• Filtering out the hype in self-driving car news to get a clear picture of its state today (6:52)
• The data required for a self-driving vehicle (13:56)
• Operation Vacation and how Aquarium can help provide the tools to make models better (16:53)
• Utilizing neural networks to index data (20:41)
• How Aquarium fits in the ML stack (30:25)
• Interesting use cases of Aquarium (33:59)
• Distinguishing subclasses of machine learning (40:05)
• Human involvement in machine learning (46:13)

The Data Stack Show is a weekly podcast powered by RudderStack. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.

RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

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