Player FM과 저희 커뮤니티의 TWIML and Sam Charrington 콘텐츠는 모두 원 저작자에게 속하며 Player FM이 아닌 작가가 저작권을 갖습니다. 오디오는 해당 서버에서 직접 스트리밍 됩니다. 구독 버튼을 눌러 Player FM에서 업데이트 현황을 확인하세요. 혹은 다른 팟캐스트 앱에서 URL을 불러오세요.

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Assessing Data Quality at Shopify with Wendy Foster - #592

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Manage episode 341600736 series 2355587
Player FM과 저희 커뮤니티의 TWIML and Sam Charrington 콘텐츠는 모두 원 저작자에게 속하며 Player FM이 아닌 작가가 저작권을 갖습니다. 오디오는 해당 서버에서 직접 스트리밍 됩니다. 구독 버튼을 눌러 Player FM에서 업데이트 현황을 확인하세요. 혹은 다른 팟캐스트 앱에서 URL을 불러오세요.

Today we’re back with another installment of our Data-Centric AI series, joined by Wendy Foster, a director of engineering & data science at Shopify. In our conversation with Wendy, we explore the differences between data-centric and model-centric approaches and how they manifest at Shopify, including on her team, which is responsible for utilizing merchant and product data to assist individual vendors on the platform. We discuss how they address, maintain, and improve data quality, emphasizing the importance of coverage and “freshness” data when solving constantly evolving use cases. Finally, we discuss how data is taxonomized at the company and the challenges that present themselves when producing large-scale ML models, future use cases that Wendy expects her team to tackle, and we briefly explore Merlin, Shopify’s new ML platform (that you can hear more about at TWIMLcon!), and how it fits into the broader scope of ML at the company.

The complete show notes for this episode can be found at twimlai.com/go/592

  continue reading

651 에피소드

공유
 
Manage episode 341600736 series 2355587
Player FM과 저희 커뮤니티의 TWIML and Sam Charrington 콘텐츠는 모두 원 저작자에게 속하며 Player FM이 아닌 작가가 저작권을 갖습니다. 오디오는 해당 서버에서 직접 스트리밍 됩니다. 구독 버튼을 눌러 Player FM에서 업데이트 현황을 확인하세요. 혹은 다른 팟캐스트 앱에서 URL을 불러오세요.

Today we’re back with another installment of our Data-Centric AI series, joined by Wendy Foster, a director of engineering & data science at Shopify. In our conversation with Wendy, we explore the differences between data-centric and model-centric approaches and how they manifest at Shopify, including on her team, which is responsible for utilizing merchant and product data to assist individual vendors on the platform. We discuss how they address, maintain, and improve data quality, emphasizing the importance of coverage and “freshness” data when solving constantly evolving use cases. Finally, we discuss how data is taxonomized at the company and the challenges that present themselves when producing large-scale ML models, future use cases that Wendy expects her team to tackle, and we briefly explore Merlin, Shopify’s new ML platform (that you can hear more about at TWIMLcon!), and how it fits into the broader scope of ML at the company.

The complete show notes for this episode can be found at twimlai.com/go/592

  continue reading

651 에피소드

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